Peter Brusilovsky presents research on user control in adaptive information access systems. The document discusses three types of adaptive systems - adaptive hypermedia, adaptive search, and recommender systems. It explores how each system currently handles user control and collaboration with AI, and proposes methods to improve user control and transparency. These include allowing users to control personalization parameters, fusion of multiple ranking sources, and visualizing recommendation results to better understand the reasoning process. The goal is to develop systems where AI provides information and users make informed decisions, with the human firmly in control.
Two Brains are Better than One: User Control in Adaptive Information AccessPeter Brusilovsky
In recent years, the use of Artificial Intelligence (AI) technologies expanded to many areas where they directly affect the lives of many people. AI-based approaches advise human decision-makers who should be released on bail, whether it is a good time to discharge a patient from a hospital and whether a specific student is at risk to fail a course. Such an extensive use in AI in decision making came with a range of protentional problems that have been extensively studied over the last few years. Recognition of these problems motivated a rapid rise of research on “human-centered AI”, which attempted to address and minimize the negative effects of using AI technologies. Among the ideas of human-centered AI is user control - engaging users in affecting AI decision making to prevent possible errors and biases. In my talk, I will focus on the application of user control in one popular area of AI application, adaptive information access. Adaptive information access systems such as personalized search and recommender systems attempt to model their users to help them in finding the most relevant information. Yet, user modeling and personalization mechanisms might not always work as expected resulting in errors, biases, and suboptimal behavior. Combining the decision power or AI with the ability of the user to guide and control it brings together the strong sides of artificial and human intelligence and could lead to better results. In my talk, I review several projects focused on user control in adaptive information access systems and discuss the benefits and challenges of this approach.
Interfaces for User-Controlled and Transparent RecommendationsPeter Brusilovsky
This document discusses interfaces for providing transparent and user-controlled recommendations. It outlines problems with single ranked lists and proposes solutions like explaining recommendations, visualizing relevance processes, and allowing users to explore and control personalization. Specific solutions discussed include visualization tools, open learner/user models, and interfaces that combine relevance from multiple sources and allow controlling factors. Studies found that explorable and controllable recommendations better support understanding relevance and finding relevant items.
User Control in AIED (Artificial Intelligence in Education)Peter Brusilovsky
This document summarizes research on improving user control and personalization in artificial intelligence for education (AIED) systems. It discusses several AIED systems that provide adaptive navigation support and annotation based on user models while allowing user control over sequencing and navigation. Evaluation of these systems found they can reduce effort, encourage exploration, and increase learning outcomes when users are able to follow or override advice. The document also presents approaches that improve transparency and control through open learner models, controllable ranking, visualization of recommendation models, and balancing adaptation with user exploration.
Cross discipline collaboration benefits from group think, a consolidation of soft system methodology and user focused design that all starts with design thinking that sees clients, designers, developers and information architects working together to address user problems and needs. As with any great adventure, design thinking starts with exploration and discovery.This presentation examines the high level tenants of system thinking, expands the scope of user thinking to include tools and devices that users employ to find out designs and delve into the specifics of design thinking, its methods and outcomes.
This document presents a project proposal for a Recommendation System for Technical Learning. It includes:
1. The names of the team members and project guide.
2. The objectives are to create a recommendation system to recommend relevant courses and books to users based on popularity and interests using collaborative and content-based filtering.
3. The literature review discusses previous recommendation system problems and solutions using collaborative filtering on Hadoop and considering location as an attribute.
4. The solution approach uses two types of filtering - collaborative and content-based - to build the recommendation system and analyze user ratings to train an ML model to make recommendations.
Human-Centered AI in AI-ED - Keynote at AAAI 2022 AI for Education workshopPeter Brusilovsky
This document summarizes research on human-centered AI in AI education (AIED) systems. It discusses the need for transparency, interactivity, and collaboration between humans and AI in AIED. Some key points:
1) Early "expert systems" lacked transparency and trust, motivating research on explainable, transparent, and human-centered AI.
2) Modern research aims to make learner and content models visible, allow user control of AI parameters and recommendations, and provide explanations for AI decisions.
3) Several AIED systems are discussed that collaborate with users, visualize models, and give users control over navigation, rankings, and social comparisons to improve learning outcomes.
Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open...Erasmo Purificato
Slide of the tutorial entitled "Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges" held at CIKM'23: 32nd ACM International Conference on Information and Knowledge Management (October 21, 2023 | Birmingham, United Kingdom)
This document summarizes Katrien Verbert's research into mixed-initiative recommender systems. It discusses her work on explaining recommendations to increase user trust and enabling user interaction with recommendation processes. Examples of projects include TasteWeights, a visual interactive hybrid recommender, and IntersectionExplorer, which allows users to explore recommendations from multiple perspectives. The document also outlines Verbert's studies on different aspects of interactive recommender systems like transparency, user control, and personalization.
Two Brains are Better than One: User Control in Adaptive Information AccessPeter Brusilovsky
In recent years, the use of Artificial Intelligence (AI) technologies expanded to many areas where they directly affect the lives of many people. AI-based approaches advise human decision-makers who should be released on bail, whether it is a good time to discharge a patient from a hospital and whether a specific student is at risk to fail a course. Such an extensive use in AI in decision making came with a range of protentional problems that have been extensively studied over the last few years. Recognition of these problems motivated a rapid rise of research on “human-centered AI”, which attempted to address and minimize the negative effects of using AI technologies. Among the ideas of human-centered AI is user control - engaging users in affecting AI decision making to prevent possible errors and biases. In my talk, I will focus on the application of user control in one popular area of AI application, adaptive information access. Adaptive information access systems such as personalized search and recommender systems attempt to model their users to help them in finding the most relevant information. Yet, user modeling and personalization mechanisms might not always work as expected resulting in errors, biases, and suboptimal behavior. Combining the decision power or AI with the ability of the user to guide and control it brings together the strong sides of artificial and human intelligence and could lead to better results. In my talk, I review several projects focused on user control in adaptive information access systems and discuss the benefits and challenges of this approach.
Interfaces for User-Controlled and Transparent RecommendationsPeter Brusilovsky
This document discusses interfaces for providing transparent and user-controlled recommendations. It outlines problems with single ranked lists and proposes solutions like explaining recommendations, visualizing relevance processes, and allowing users to explore and control personalization. Specific solutions discussed include visualization tools, open learner/user models, and interfaces that combine relevance from multiple sources and allow controlling factors. Studies found that explorable and controllable recommendations better support understanding relevance and finding relevant items.
User Control in AIED (Artificial Intelligence in Education)Peter Brusilovsky
This document summarizes research on improving user control and personalization in artificial intelligence for education (AIED) systems. It discusses several AIED systems that provide adaptive navigation support and annotation based on user models while allowing user control over sequencing and navigation. Evaluation of these systems found they can reduce effort, encourage exploration, and increase learning outcomes when users are able to follow or override advice. The document also presents approaches that improve transparency and control through open learner models, controllable ranking, visualization of recommendation models, and balancing adaptation with user exploration.
Cross discipline collaboration benefits from group think, a consolidation of soft system methodology and user focused design that all starts with design thinking that sees clients, designers, developers and information architects working together to address user problems and needs. As with any great adventure, design thinking starts with exploration and discovery.This presentation examines the high level tenants of system thinking, expands the scope of user thinking to include tools and devices that users employ to find out designs and delve into the specifics of design thinking, its methods and outcomes.
This document presents a project proposal for a Recommendation System for Technical Learning. It includes:
1. The names of the team members and project guide.
2. The objectives are to create a recommendation system to recommend relevant courses and books to users based on popularity and interests using collaborative and content-based filtering.
3. The literature review discusses previous recommendation system problems and solutions using collaborative filtering on Hadoop and considering location as an attribute.
4. The solution approach uses two types of filtering - collaborative and content-based - to build the recommendation system and analyze user ratings to train an ML model to make recommendations.
Human-Centered AI in AI-ED - Keynote at AAAI 2022 AI for Education workshopPeter Brusilovsky
This document summarizes research on human-centered AI in AI education (AIED) systems. It discusses the need for transparency, interactivity, and collaboration between humans and AI in AIED. Some key points:
1) Early "expert systems" lacked transparency and trust, motivating research on explainable, transparent, and human-centered AI.
2) Modern research aims to make learner and content models visible, allow user control of AI parameters and recommendations, and provide explanations for AI decisions.
3) Several AIED systems are discussed that collaborate with users, visualize models, and give users control over navigation, rankings, and social comparisons to improve learning outcomes.
Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open...Erasmo Purificato
Slide of the tutorial entitled "Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges" held at CIKM'23: 32nd ACM International Conference on Information and Knowledge Management (October 21, 2023 | Birmingham, United Kingdom)
This document summarizes Katrien Verbert's research into mixed-initiative recommender systems. It discusses her work on explaining recommendations to increase user trust and enabling user interaction with recommendation processes. Examples of projects include TasteWeights, a visual interactive hybrid recommender, and IntersectionExplorer, which allows users to explore recommendations from multiple perspectives. The document also outlines Verbert's studies on different aspects of interactive recommender systems like transparency, user control, and personalization.
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Acc...Erasmo Purificato
Slide of the Tutorial on "User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives" @ UMAP'23: 31st ACM Conference on User Modeling, Adaptation and Personalization (June 26, 2023 | Limassol, Cyprus)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document summarizes an article on e-learning recommendation systems. It begins by defining recommendation systems and their use in helping learners identify suitable learning resources. It then provides a brief history of recommendation systems and discusses popular approaches like collaborative filtering, content-based filtering, and hybrid filtering. The document focuses on e-learning recommendation systems, reviewing key works that examine factors like context and setting. It also summarizes specific e-learning recommendation system frameworks and a study measuring learner performance with such systems.
This document summarizes Katrien Verbert's talk on mixed-initiative recommender systems at the 12th RecSysNL meetup. It discusses how recommender systems can increase user trust and acceptance by explaining recommendations and enabling user interaction with the recommendation process. Examples of Verbert's research include systems like TasteWeights and IntersectionExplorer that provide transparency, user control, and support for exploration in recommender interfaces. Verbert's work also examines how personal characteristics affect user experience with different types and levels of recommender system controllability.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Human-centered AI: how can we support lay users to understand AI?Katrien Verbert
The document summarizes research on human-centered AI and how to support lay users in understanding AI. It discusses various research projects that aim to explain model outcomes to increase user trust and acceptance. It explores how personal characteristics like need for cognition can impact the effectiveness of explanations. The research also looks at different application domains for AI like healthcare, education, agriculture and recommendations. It emphasizes the importance of user involvement, personalization and domain expertise in developing AI systems that non-experts can understand and trust.
This document summarizes Katrien Verbert's presentation on interactive recommender systems. The presentation covered several topics:
1) Different types of recommendation techniques including collaborative filtering, content-based filtering, and knowledge-based filtering.
2) Research on interactive recommender systems that aim to increase transparency, user control, and diversity of recommendations.
3) Several user studies conducted on interactive recommender systems that explored talks and conferences, finding that explanations and various levels of user control can impact user experience.
This document summarizes a presentation given by Katrien Verbert on interactive recommender systems. It provides an overview of Verbert's research group at KU Leuven, which focuses on recommender systems, visualization, and intelligent user interfaces. The presentation describes various techniques for building interactive recommender systems, including explaining recommendations to users, enabling user interaction with the recommendation process, and addressing challenges like diversity, cold starts, and context awareness. It also summarizes several studies conducted by Verbert and collaborators on interactive music and research talk recommender systems.
Personalization in the Context of Relevance-Based VisualizationPeter Brusilovsky
In this talk, I will review our research attempts to
implement different kinds of personalization in the context
of relevance-based visualization. The goal of this research
stream is to make relevance-based visualization adaptive to
user long-term goals, interests, or prospects rather just
responsive to short term immediate needs such as query
terms. I will present four personalized relevance-based
visualization systems: Adaptive VIBE, TalkExplorer,
SetFusion, and IntersectionExplorer, For each system, I
will present its idea, some evaluation results, and
lessons learned.
https://doi.org/10.1145/3038462.3038474
Data, Data Everywhere: What's A Publisher to Do?Anita de Waard
The document discusses publishers' roles in data sharing and challenges in open science. It notes that while most scientists agree access to others' data would benefit research, fewer are willing to share their own data due to lack of training and incentives. Publishers are working to establish data sharing guidelines and integrate platforms to store, share, and analyze research data and tools. However, many questions remain around publishing data science given distributed and interconnected data, tools, and knowledge networks. Publishers will need to transition from pipelines to platforms and enable these new network effects.
What is e-research?
Enhancing research practice
e-Research Methods, Strategies, and Issues
Tips For Finding Useful Information
Some Search Tools for doing e-research
Research Design
Quantitative Research
Qualitative Research
Ethics & The e-Researcher
How The Net Complicates Ethics?
Privacy, Confidentiality, Autonomy, And The Respect For Persons
Tips For Ethical e-Research
Collaboration Tools
Why Consensus?
Net-based dissemination of E-research results
Dissemination through peer-reviewed articles
Advantages of a peer-reviewed article
Dissemination through email lists or Usenet groups
Dissemination through a virtual conference
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Recommendations for Open Online Education: An Algorithmic StudyHendrik Drachsler
Recommending courses to students in online platforms is studied widely. Almost all studies target closed platforms, that belong to a University or some other educational provider. This makes the course recommenders situation specific. Over the last years, a demand has developed for recommender system that suit open online platforms. Those platforms have some common characteristics, such as the lack of rich user profiles with content metadata. Instead they log user interactions within the platform that can be used for analysis and personalization. In this paper, we investigate how user interactions and activities tracked within open online learning platforms can be used to provide recommendations. We present a study in which we investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. We use data from the OpenU open online learning platform that is in use by the Open University of the Netherlands. The results show that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system proves to outperform the classical approaches on prediction accuracy of recommendations in terms of recall. We conclude that, if the algorithms are chosen wisely, recommenders can contribute to a better experience of learners in open online courses.
Soude Fazeli, Enayat Rajabi, Leonardo Lezcano, Hendrik Drachsler, Peter Sloep
The document discusses the Fluid Project, which aims to develop a flexible user interface framework and component library to improve the user experience and accessibility of open source education applications like Sakai, uPortal, Moodle, and Kuali Student. The project will involve usability evaluations, development of reusable UI components focused on key areas of improvement, and creation of a framework to support flexible and customizable interfaces while maintaining accessibility. Initial work will focus on a "lightbox" component to improve image organization and reordering in Sakai. The goal is to foster collaboration across projects and communities to incrementally enhance the user experience.
Human Interfaces to Artificial Intelligence in EducationPeter Brusilovsky
Human Interfaces to Artificial Intelligence in Education discusses:
1) The need for transparency, interactivity, and human-centered design when developing AI systems for education to address issues like lack of ability to inspect AI decisions and lack of trust in AI recommendations.
2) Approaches like explainable AI, visualizing learner models and domain models, and natural communication with AI systems to provide transparency and user control.
3) Examples of open learner model visualizations and explanatory recommendations that make learner knowledge and AI recommendations more transparent.
Scalable Exploration of Relevance Prospects to Support Decision MakingKatrien Verbert
Presented at IntRS 2016 - Interfaces and Human Decision Making for Recommender Systems, workshop at RecSys 2016
Citation: Verbert, K., Seipp, K., He, C., Parra, D., Wongchokprasitti, C., & Brusilovsky, P. (2016). Scalable Exploration of Relevance Prospects to Support Decision Making. Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2016), Boston, MA, USA, September 16, 2016.
Interactive recommender systems: opening up the “black box”Katrien Verbert
This document summarizes a presentation given by Katrien Verbert on interactive recommender systems. It discusses how recommender systems are typically "black boxes" that do not explain their recommendations to users. The presentation aims to open up this black box by exploring ways to increase transparency, user control, and interaction with recommender systems. Examples of interactive recommender systems that allow users to explore the recommendation process and provide explanations are described. Research on developing and evaluating such interactive systems through multiple user studies is summarized. The objective is to enhance user trust and engagement with recommender systems.
A Literature Survey on Recommendation Systems for Scientific Articles.pdfAmber Ford
This document summarizes a literature survey on recommendation systems for scientific articles. It begins by outlining problems faced by researchers, including information overload from searching large amounts of non-structured data. It then reviews different types of recommender systems, including content-based, collaborative, knowledge-based, semantic-based, and hybrid approaches. The objective of the survey is to develop a framework for a semantic-based recommender system that integrates ontologies to help researchers more efficiently find relevant scientific articles.
This document provides an overview of recommender systems. It discusses how recommender systems aim to help users find items online that match their interests. It describes two main approaches for recommender systems - collaborative filtering and content-based filtering. Collaborative filtering looks at users' past behaviors and items to find similarities between users and make recommendations. Content-based filtering uses item attributes and properties to recommend similar items to users. The document also discusses challenges with existing recommender systems and how different techniques can be combined in hybrid systems.
Program code examples (known also as worked examples) play a crucial role in learning how to program. Instructors use examples extensively to demonstrate the semantics of the programming language being taught and to highlight the fundamental coding patterns. Programming textbooks allocate considerable space to present and explain code examples. To make the process of studying code examples more interactive, CS education researchers developed a range of tools to engage students in the study of code examples. These tools include codecasts (codemotion,codecast,elicasts), interactive example explorers (WebEx, PCEX), and tutoring systems (DeepTutor). An important component in all types of worked examples is code explanations associated with specific code lines or code chunks of an example. The explanations connect examples with general programming knowledge explaining the role and function of code fragments or their behavior. In textbooks, these explanations are usually presented as comments in the code or as explanations on the margins. The example explorer tools allow students to examine these explanations interactively. Tutoring systems, which engage students in explaining the code, use these model explanations to check student responses and provide scaffolding. In all these cases, to make a worked example re-usable beyond its presentation in a lecture, the explanations have to be authored by instructors or domain experts i.e., produced and integrated into a specific system. As the experience of the last 10 years demonstrated, these explanations are hard to obtain. Those already collected are usually “locked” in a specific example-focused system and can’t be reused. The purpose of this working group is to support broader re-used of worked examples augmented with explanations. Our current plan is to develop а standard approach to represent explained examples. This approach will enable an example created for any of the existing systems to be explored in a standard format and imported into any other example-focused system. We plan to follow a successful experience of the PEML working group focused on re-using programming exercises.
SANN: Programming Code Representation Using Attention Neural Network with Opt...Peter Brusilovsky
Slides of CIKM 2023 paper by Muntasir Hoq, Sushanth Reddy Chilla, Melika Ahmadi Ranjbar, Peter Brusilovsky and Bita Akram
https://dl.acm.org/doi/10.1145/3583780.3615047
Mais conteúdo relacionado
Semelhante a User Control in Adaptive Information Access
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Acc...Erasmo Purificato
Slide of the Tutorial on "User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives" @ UMAP'23: 31st ACM Conference on User Modeling, Adaptation and Personalization (June 26, 2023 | Limassol, Cyprus)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document summarizes an article on e-learning recommendation systems. It begins by defining recommendation systems and their use in helping learners identify suitable learning resources. It then provides a brief history of recommendation systems and discusses popular approaches like collaborative filtering, content-based filtering, and hybrid filtering. The document focuses on e-learning recommendation systems, reviewing key works that examine factors like context and setting. It also summarizes specific e-learning recommendation system frameworks and a study measuring learner performance with such systems.
This document summarizes Katrien Verbert's talk on mixed-initiative recommender systems at the 12th RecSysNL meetup. It discusses how recommender systems can increase user trust and acceptance by explaining recommendations and enabling user interaction with the recommendation process. Examples of Verbert's research include systems like TasteWeights and IntersectionExplorer that provide transparency, user control, and support for exploration in recommender interfaces. Verbert's work also examines how personal characteristics affect user experience with different types and levels of recommender system controllability.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Human-centered AI: how can we support lay users to understand AI?Katrien Verbert
The document summarizes research on human-centered AI and how to support lay users in understanding AI. It discusses various research projects that aim to explain model outcomes to increase user trust and acceptance. It explores how personal characteristics like need for cognition can impact the effectiveness of explanations. The research also looks at different application domains for AI like healthcare, education, agriculture and recommendations. It emphasizes the importance of user involvement, personalization and domain expertise in developing AI systems that non-experts can understand and trust.
This document summarizes Katrien Verbert's presentation on interactive recommender systems. The presentation covered several topics:
1) Different types of recommendation techniques including collaborative filtering, content-based filtering, and knowledge-based filtering.
2) Research on interactive recommender systems that aim to increase transparency, user control, and diversity of recommendations.
3) Several user studies conducted on interactive recommender systems that explored talks and conferences, finding that explanations and various levels of user control can impact user experience.
This document summarizes a presentation given by Katrien Verbert on interactive recommender systems. It provides an overview of Verbert's research group at KU Leuven, which focuses on recommender systems, visualization, and intelligent user interfaces. The presentation describes various techniques for building interactive recommender systems, including explaining recommendations to users, enabling user interaction with the recommendation process, and addressing challenges like diversity, cold starts, and context awareness. It also summarizes several studies conducted by Verbert and collaborators on interactive music and research talk recommender systems.
Personalization in the Context of Relevance-Based VisualizationPeter Brusilovsky
In this talk, I will review our research attempts to
implement different kinds of personalization in the context
of relevance-based visualization. The goal of this research
stream is to make relevance-based visualization adaptive to
user long-term goals, interests, or prospects rather just
responsive to short term immediate needs such as query
terms. I will present four personalized relevance-based
visualization systems: Adaptive VIBE, TalkExplorer,
SetFusion, and IntersectionExplorer, For each system, I
will present its idea, some evaluation results, and
lessons learned.
https://doi.org/10.1145/3038462.3038474
Data, Data Everywhere: What's A Publisher to Do?Anita de Waard
The document discusses publishers' roles in data sharing and challenges in open science. It notes that while most scientists agree access to others' data would benefit research, fewer are willing to share their own data due to lack of training and incentives. Publishers are working to establish data sharing guidelines and integrate platforms to store, share, and analyze research data and tools. However, many questions remain around publishing data science given distributed and interconnected data, tools, and knowledge networks. Publishers will need to transition from pipelines to platforms and enable these new network effects.
What is e-research?
Enhancing research practice
e-Research Methods, Strategies, and Issues
Tips For Finding Useful Information
Some Search Tools for doing e-research
Research Design
Quantitative Research
Qualitative Research
Ethics & The e-Researcher
How The Net Complicates Ethics?
Privacy, Confidentiality, Autonomy, And The Respect For Persons
Tips For Ethical e-Research
Collaboration Tools
Why Consensus?
Net-based dissemination of E-research results
Dissemination through peer-reviewed articles
Advantages of a peer-reviewed article
Dissemination through email lists or Usenet groups
Dissemination through a virtual conference
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Recommendations for Open Online Education: An Algorithmic StudyHendrik Drachsler
Recommending courses to students in online platforms is studied widely. Almost all studies target closed platforms, that belong to a University or some other educational provider. This makes the course recommenders situation specific. Over the last years, a demand has developed for recommender system that suit open online platforms. Those platforms have some common characteristics, such as the lack of rich user profiles with content metadata. Instead they log user interactions within the platform that can be used for analysis and personalization. In this paper, we investigate how user interactions and activities tracked within open online learning platforms can be used to provide recommendations. We present a study in which we investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. We use data from the OpenU open online learning platform that is in use by the Open University of the Netherlands. The results show that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system proves to outperform the classical approaches on prediction accuracy of recommendations in terms of recall. We conclude that, if the algorithms are chosen wisely, recommenders can contribute to a better experience of learners in open online courses.
Soude Fazeli, Enayat Rajabi, Leonardo Lezcano, Hendrik Drachsler, Peter Sloep
The document discusses the Fluid Project, which aims to develop a flexible user interface framework and component library to improve the user experience and accessibility of open source education applications like Sakai, uPortal, Moodle, and Kuali Student. The project will involve usability evaluations, development of reusable UI components focused on key areas of improvement, and creation of a framework to support flexible and customizable interfaces while maintaining accessibility. Initial work will focus on a "lightbox" component to improve image organization and reordering in Sakai. The goal is to foster collaboration across projects and communities to incrementally enhance the user experience.
Human Interfaces to Artificial Intelligence in EducationPeter Brusilovsky
Human Interfaces to Artificial Intelligence in Education discusses:
1) The need for transparency, interactivity, and human-centered design when developing AI systems for education to address issues like lack of ability to inspect AI decisions and lack of trust in AI recommendations.
2) Approaches like explainable AI, visualizing learner models and domain models, and natural communication with AI systems to provide transparency and user control.
3) Examples of open learner model visualizations and explanatory recommendations that make learner knowledge and AI recommendations more transparent.
Scalable Exploration of Relevance Prospects to Support Decision MakingKatrien Verbert
Presented at IntRS 2016 - Interfaces and Human Decision Making for Recommender Systems, workshop at RecSys 2016
Citation: Verbert, K., Seipp, K., He, C., Parra, D., Wongchokprasitti, C., & Brusilovsky, P. (2016). Scalable Exploration of Relevance Prospects to Support Decision Making. Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2016), Boston, MA, USA, September 16, 2016.
Interactive recommender systems: opening up the “black box”Katrien Verbert
This document summarizes a presentation given by Katrien Verbert on interactive recommender systems. It discusses how recommender systems are typically "black boxes" that do not explain their recommendations to users. The presentation aims to open up this black box by exploring ways to increase transparency, user control, and interaction with recommender systems. Examples of interactive recommender systems that allow users to explore the recommendation process and provide explanations are described. Research on developing and evaluating such interactive systems through multiple user studies is summarized. The objective is to enhance user trust and engagement with recommender systems.
A Literature Survey on Recommendation Systems for Scientific Articles.pdfAmber Ford
This document summarizes a literature survey on recommendation systems for scientific articles. It begins by outlining problems faced by researchers, including information overload from searching large amounts of non-structured data. It then reviews different types of recommender systems, including content-based, collaborative, knowledge-based, semantic-based, and hybrid approaches. The objective of the survey is to develop a framework for a semantic-based recommender system that integrates ontologies to help researchers more efficiently find relevant scientific articles.
This document provides an overview of recommender systems. It discusses how recommender systems aim to help users find items online that match their interests. It describes two main approaches for recommender systems - collaborative filtering and content-based filtering. Collaborative filtering looks at users' past behaviors and items to find similarities between users and make recommendations. Content-based filtering uses item attributes and properties to recommend similar items to users. The document also discusses challenges with existing recommender systems and how different techniques can be combined in hybrid systems.
Semelhante a User Control in Adaptive Information Access (20)
Program code examples (known also as worked examples) play a crucial role in learning how to program. Instructors use examples extensively to demonstrate the semantics of the programming language being taught and to highlight the fundamental coding patterns. Programming textbooks allocate considerable space to present and explain code examples. To make the process of studying code examples more interactive, CS education researchers developed a range of tools to engage students in the study of code examples. These tools include codecasts (codemotion,codecast,elicasts), interactive example explorers (WebEx, PCEX), and tutoring systems (DeepTutor). An important component in all types of worked examples is code explanations associated with specific code lines or code chunks of an example. The explanations connect examples with general programming knowledge explaining the role and function of code fragments or their behavior. In textbooks, these explanations are usually presented as comments in the code or as explanations on the margins. The example explorer tools allow students to examine these explanations interactively. Tutoring systems, which engage students in explaining the code, use these model explanations to check student responses and provide scaffolding. In all these cases, to make a worked example re-usable beyond its presentation in a lecture, the explanations have to be authored by instructors or domain experts i.e., produced and integrated into a specific system. As the experience of the last 10 years demonstrated, these explanations are hard to obtain. Those already collected are usually “locked” in a specific example-focused system and can’t be reused. The purpose of this working group is to support broader re-used of worked examples augmented with explanations. Our current plan is to develop а standard approach to represent explained examples. This approach will enable an example created for any of the existing systems to be explored in a standard format and imported into any other example-focused system. We plan to follow a successful experience of the PEML working group focused on re-using programming exercises.
SANN: Programming Code Representation Using Attention Neural Network with Opt...Peter Brusilovsky
Slides of CIKM 2023 paper by Muntasir Hoq, Sushanth Reddy Chilla, Melika Ahmadi Ranjbar, Peter Brusilovsky and Bita Akram
https://dl.acm.org/doi/10.1145/3583780.3615047
This document discusses various forms of "smart content" that can be used in computer science education to engage students in meaningful learning activities through interaction. It describes different types of smart content including interactive coding problems, program visualizations, and worked examples. It also discusses frameworks for providing levels of support, feedback, and assessment for problems, examples, and coding activities. Finally, it discusses how adaptive learning systems can utilize student data and smart content to provide personalized navigation support, recommendations, and engagement.
Personalized Learning: Expanding the Social Impact of AIPeter Brusilovsky
Slide of my keynote talk at SIAIA '23 workshop held at AAAI 2023:
The use of AI in Education could be traced to the early days of AI. While the publicity associated with the most recent wave of AI applications rarely mentions education, it is through the improvement in education AI could achieve an impressive social impact. In particular, the AI ability to personalize the learning process could make a large difference in a context where learners' knowledge could be radically different from learner to learner. Modern computer and internet technologies can now bring the power of learning in the forms of MOOCs, online textbooks, and zoom courses truly worldwide. Yet, without personalization, the potential of these technologies is not fully leveraged. In this talk, I will review several generations of research on personalized learning and discuss tools, technologies, and infrastructures for personalized learning that we are currently exploring.
Action Sequence Mining and Behavior Pattern Analysis for User ModelingPeter Brusilovsky
Slides of my talk at 2022 Workshop on Temporal Aspects of User Modeling
Tracing learner interaction with educational content has recently emerged as a centerpiece of learning analytics. Augmented by various data mining technologies, learner data has been used to predict learner success and failure, prevent dropouts, and inform university officials about student progress. While the majority of existing learning analytics approaches ignore the time aspect in the learning data, recent research indicated that not just what the learners do, but how and in which order they do it is critical to understand differences between learners, model their behavior, and predict their performance. In my talk, I will focus on the application of action sequence mining as a tool to extract temporal patterns of learning behavior and recognize cohorts of learners with divergent behavior. I will review three case studies of using sequence mining with learner data, present the obtained results, and discuss their importance for user modeling and personalization.
The Return of Intelligent Textbooks - ITS 2021 keynote talkPeter Brusilovsky
Early research on hypermedia learning and Web-based education featured a strong stream of work on intelligent and adaptive textbooks, which combined the knowledge modeling ideas from the field of intelligent tutoring with rich linking offered by the hypermedia and the Web. However, over the next ten years from 2005 to 2015 this area was relatively quiet as the focus of research in e-learning has shifted to other topics and other creative ideas to leverage the power of Internet. A recent gradual shift of the whole publication industry from printed books to electronic books followed by a rapid growth or the volume of online books re-ignited interests to “more intelligent” textbooks. The research on the new generation of intelligent textbooks engaged a larger set of technologies and engaged scholars from a broader range of areas including machine learning, natural language understanding, social computing, etc. In my talk I will review the past and present of research on intelligent textbooks from its origins to the diverse modern work providing examples of most interesting technologies and research results.
Data-Driven Education 2020: Using Big Educational Data to Improve Teaching an...Peter Brusilovsky
Modern educational settings from regular classrooms to MOOCs produce a a rapidly increasing volume of data that captures individual learning progress of millions of students at different level of granularity. This presence of this data opens a unique opportunity to re-engineer traditional education and build and develop a range of efficient data-driven approaches to support teaching and learning. In my talk, I will present several ways to use big educational data explored in our lab. The focus will be on open social learning modeling and identifying individual differences through sequential pattern mining, but several other approaches will be mentioned. Open social learning modeling and sequential pattern mining provides two considerably different examples on using educational data. One offers an immediate use of class interaction history to develop more engaging content access while another shows how big data could be used to uncover important individual differences that could be used to optimize the process for individual leaners.
Personalized Online Practice Systems for Learning ProgrammingPeter Brusilovsky
An adaptive learning system called Mastery Grids was created to increase student engagement with online educational content by incorporating personalized and social adaptive features. Mastery Grids uses open learner modeling to display a student's knowledge progress compared to their peers, adaptive navigation support to guide students to relevant activities, and concept-based recommendations of content. A study found that Mastery Grids significantly increased student success rates, time spent engaging with content, and learning compared to non-adaptive systems. Further research added direct recommendations to Mastery Grids and found they increased transparency and led to more efficient learning when explanations of recommendations were provided through the open learner model visualizations.
UMAP 2019 talk Evaluating Visual Explanations for Similarity-Based Recommenda...Peter Brusilovsky
Tsai, Chun-Hua, and Peter Brusilovsky. 2019. "Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance." In the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, 22-30. Larnaca, Cyprus: ACM.
Course-Adaptive Content Recommender for Course AuthoringPeter Brusilovsky
Developing online courses is a complex and time-consuming
process that involves organizing a course into a sequence of topics and
allocating the appropriate learning content within each topic. This task
is especially difficult in complex domains like programming, due to the
incremental nature of programming knowledge, where new topics extensively
build upon domain concepts that were introduced in earlier lessons.
In this paper, we propose a course-adaptive content-based recommender
system that assists course authors and instructors in selecting the most
relevant learning material for each course topic. The recommender system
adapts to the deep prerequisite structure of the course as envisioned
by a specific instructor, while unobtrusively deducing that structure from
problem-solving examples that the instructor uses to present course concepts.
We assessed the quality of recommendations and examined several
aspects of the recommendation process by using three datasets collected
from two different courses.While the presented recommender system was
built for the domain of introductory programming, our course-adaptive
recommendation approach could be used in a variety of other domains.
Data-Driven Education: Using Big Educational Data to Improve Teaching and Learning. Keynote slides for 15th International Conference on Web-Based Learning, ICWL 2016, Rome, Italy, October 26–29.
From Expert-Driven to Data-Driven Adaptive LearningPeter Brusilovsky
Keynote slides for the Workshop on Advancing Education with Data at the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, Aug 14, 2017
Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and ...Peter Brusilovsky
This study examined whether student stereotypes could be used to improve predictions of problem-solving performance in MOOCs. The researchers tested simple stereotypes based on demographics and performance, but found they did not yield accurate models of different student groups' learning. More advanced stereotypes based on patterns of problem-solving behavior also did not distinguish groups with significantly different learning models. The findings suggest stereotypes may not effectively represent the finer-grained differences in how students approach learning. Overall, the study did not find evidence that stereotype models can improve predictions of problem-solving over alternative models of individual student learning.
IUI2017 SmartLearn keynote: Intelligent Interfaces for Open Social Student M...Peter Brusilovsky
In this talk I will introduce the emerging technology of
Open Social Student Modeling (OSSM) and review several
projects performed in our research lab to investigate the
potential of OSSM.
OSSM is a recent extension of Open Student Modeling
(OSM), a popular technology in the area of personalized
learning systems. While in traditional personalized systems,
student models were hidden “under the hood” and used to
personalize the educational process; open student modeling
introduced the ability to view and modify the state of
students’ own knowledge to support reflection, selforganized
learning, and system transparency. Open Social
Student Modeling takes this idea one step further by
allowing students to explore each other’s models or an
aggregated model of the class. The idea to make OSM
social was originally suggested and explored by Bull [1; 2].
Over the last few years, our team explored several
approaches to present OSSM in a highly visual form and
evaluated these approaches in a sequence of classroom and
lab studies. I will present a summary of this work
introducing such systems as QuizMap [3], Progressor [4],
and Mastery Grids [5] and reviewing most interesting
research evidence collected by the studies.
Adaptive Navigation Support and Open Social Learner Modeling for PALPeter Brusilovsky
This presentation is an overview of Open Social Learner Modeling project. It presents Mastery Grids interface, distributed personalized learning architecture Aggregate, and smart content for Java, Python, and SQL
What Should I Do Next? Adaptive Sequencing in the Context of Open Social Stu...Peter Brusilovsky
This document summarizes research on adaptive sequencing in open social student modeling. It describes how combining knowledge-based guidance with social guidance can encourage non-sequential navigation, increase learning speed for strong students, and positively relate to student performance. A classroom study found that adaptive sequencing increased learning speed and the odds of correct problem solving. Students also provided positive subjective feedback about recommendations. Future work aims to explore alternative visualization and awareness techniques.
Slides for invited talk: Brusilovsky, P. (2003) From adaptive hypermedia to the adaptive Web. In: J. Ziegler and G. Szwillus (eds.) Interaktion in Bewegung. (Proceedings of Mensch & Computer 2003, Stuttgart, September 7-10, 2003) Stuttgart, Germany: B. G. Teubner, pp. 21-2
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
1. Two Brains are Better than One:
User Control in Adaptive
Information Access
Peter Brusilovsky
PAWS Lab
School of Computing and Information
University of Pittsburgh
5. User / AI control in 3 types of IA
Adaptive HM Adaptive Search Recommendation
User driven
How to add AI?
How to control it?
Some user
control (query)
How to improve?
Fully AI Driven
Add control
Collaborate
8. Navigation vs. Adaptive Sequencing
10
Human makes navigation decision AI makes navigation decision
9. Adaptive Navigation Support: Goals
• Guidance: Help me to find what I need!
– Local guidance (“next best”)
– Global guidance (“ultimate goal”)
• Orientation: Where am I?
– Local orientation support (local area)
– Global orientation support (whole hyperspace)
10. Adaptive Navigation Support
AI provides information,
human makes an informed decision
AI is fully present
Human is in control
12. ELM-ART: Adaptive Annotation (1996)
Weber,
G.
and
Brusilovsky,
P.
(2001)
ELM-ART:
An
adaptive
versatile
system
for
Web-based
instruction.
International
Journal
of
Artificial
Intelligence
in
Education
12
(4),
351-384.
15. ANS vs Search/Recommendations
• Presentation
– In-context guidance vs. generated ranked list
• Power
– Multipe personalization engines in ANS, relevance
engine in IR
– ANS can display simultaneously several aspects of
importance/interest/relevance
– Ranking used in recommendation approaches can
express only one dimension
16. More Control! Open Learner Model
Weber, G. and Brusilovsky, P. (2001) ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of
Artificial Intelligence in Education 12 (4), 351-384.
17. Open Learner Models
Bull, S., Brusilovsky, P., and Guerra, J. (2018) Which Learning Visualisations to Offer Students? In: V. Pammer-Schindler, M. Pérez-Sanagustín, H.
Drachsler, R. Elferink and M. Scheffel (eds.) Proceedings of 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK,
September 3–5, 2018, Springer, pp. 524–530.
18. Adaptive Annotation Can:
• Reduce navigation efforts
• Reduce repetitive visits to learning content
pages
• Encourage non-sequential navigation
• Increase learning outcome
• For those who is ready to follow and advice
• Make system more attractive for students
• Students stay much longer without any reward
20. Adaptive Presentation: Goals
• Provide the different content for users
with different knowledge, goals,
background
• Provide additional material for some
categories of users
– comparisons
– extra explanations
– details
• Remove irrelevant or already known
content
21. AP: NL Generation in PEBA-II
Milosavljevic, M. (1997) Augmenting the user's knowledge via comparison. In: A. Jameson, C. Paris and C. Tasso (eds.)
Proceedings of 6th International Conference on User Modeling, UM97, Chia Laguna, Sardinia, Italy, June 2-5, 1997,
SpringerWienNewYork, pp. 119-130.
24. User Control: Scrutable
Adaptive Presentation in SASY
Czarkowski, M. and Kay, J. (2002) A scrutable adaptive hypertext. In: P. De Bra, P. Brusilovsky and R. Conejo (eds.)
Proceedings of Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH'2002),
Málaga, Spain, May 29-31, 2002, pp. 384-387.
26. More User Control: Open Model
Tsandilas, T. and M. C. schraefel (2004). "Usable adaptive hypermedia systems." New Review in Hypermedia and
Multimedia 10(1): 5.
27. Adaptive presentation: Evaluation
• Decrease reading comprehension time
• Increase learning outcome
• No effect for navigation overhead - time,
number of nodes visited, number of
operations
29. Search vs. Recommendation
• Adaptive Search
– Results are generated on the basis of user
query and user profile
– Some user control / collaboration is
embedded (query!) – the system has an idea
what the user wants now
• Personalized Recommendation
– Results are generated on the basis of user
profile alone - no control, no collaboration
31. • Compromise between several sources of relevance
– Items might be relevant for to the user profile or query
for different reasons
• Single-source: different parts/aspects of the profile
• Hybrid: different sources of information or approaches
• Hard to get universally perfect ranking
– A recommendation approach is tuned to an
overall/generic situation, but users could consult
recommendation for different needs
– Some profile aspects, sources, approaches are less
relevant in the current context, but some are more
33
While Single Ranked List is A Problem?
32. What are Possible Solutions?
• Control (Keep the ranked list, better engage users)
– Change user profile
– Change parameters (how personalization is produced)
• Visualize and Explore (Go beyond the ranked list)
– Present items visually
– Make the ranking/relevance process more transparent
– Allow users to change presentation parameters, play
with the results, better understand the process, isolate
most relevant results
34
33. What Can Be Controlled?
35
Profile Generation Presentation
User Model Ranking
Source Fusion
EXPLORE!
34. Simple Ranking Control
Allow the user to control how the ranking list is produced to adapt
personalization for the current context as well as better explore
recommendation results
37
35. How Ranking is Generated?
• [your profile] + [your query] +
personalization engine = ranked list
• Control fusion – profile vs query
• Control query (current needs)
• Control profile (generic preferences)
• Control the engine
38
36. Should we use profile or query?
• AI approach: Everything is done by AI
– use ML to classify queries into those where profile
is good and those where it is not
– White, R. W., Bennett, P., and Dumais, S. (2010) Predicting
Short-Term Interests Using Activity-Based Search Context.
In: Proceedings of the 19th ACM conference on Information
and knowledge management (CIKM 2010), Toronto, Canada,
October 2010 ACM, pp. 1009-1018.
• Human-AI collaboration approach:
– User decides whether to use personalization, AI
does the job
– TaskSieve
37. TaskSieve: Controllable Personalized Search
Ahn, Jae-wook, Peter Brusilovsky, Daqing He, Jonathan Grady, and Qi Li. 2008. "Personalized Web Exploration with Task Models." In the
17th international conference on World Wide Web, WWW '08, 1-10. Beijing, China: ACM.
38. TaskSieve Controllable Ranking
• Combine query relevance and task relevance
– Alpha * Task_Model_Score + (1-alpha) * Search
Score
– Alpha : user control (0.0, 0.5, or 1.0)
• Results
– Better than regular adaptive search
– Better then non adaptive baseline even in cases
when profile was excluded
– Users were really good in deciding when to engage
the profile and how
41
39. O'Donovan, John, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. "PeerChooser: visual interactive recommendation."
In Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, 1085-88. Florence, Italy: ACM.
PeerChooser: Control the Engine
42
40. YourNews: Control the Profile
Ahn, J.-w., Brusilovsky, P., Grady, J., He, D., and Syn, S. Y. (2007) Open user profiles for adaptive news systems: help or harm? In: 16th
international conference on World Wide Web, WWW '07, Banff, Canada, May 8-12, 2007, ACM, pp. 11-20
Open user
model
41. Concept-Level Open User Model
(SciNet)
44
Glowacka, Dorota, Tuukka Ruotsalo, Ksenia Konuyshkova, Kumaripaba Athukorala, Samuel Kaski, and Giulio Jacucci. 2013.
"Directing Exploratory Search: Reinforcement Learning from User Interactions with Keywords." In international conference on
Intelligent user interfaces, IUI '2013, 117-27. Santa Monica, USA: ACM Press.
42. Movie Tuner: Control Current Interests
45
Vig,
J.,
Sen,
S.,
and
Riedl,
J.
(2012)
The
Tag
Genome:
Encoding
Community
Knowledge
to
Support
Novel
Interaction.
ACM
Transactions
on
Interactive
Intelligent
Systems
2
(3),
Article
13.
43. uRank: Fine Control of Interests
di Sciascio, C., Sabol, V., and Veas, E. E. (2016) Rank As You Go: User-Driven Exploration of Search Results.
In: Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI '16), Sonoma, California, pp. 118-129.
44. Control and Transparency:
Two Sides of the Same Coin
Explain Visualize
Explore
Control
48
Transparency
Controllability
No full transparency
without controllability
Control is challenging
without transparency
45. TasteWeights: Profile and Mechanism
Control + Transparency
49
Knijnenburg, Bart P., Svetlin Bostandjiev, John O'Donovan, and Alfred Kobsa. 2012. "Inspectability and Control in Social Recommenders." In 6th ACM
Conference on Recommender System, 43-50. Dublin, Ireland.
46. Multiple Sources of Relevance
• Conference Navigator System for conference support (2010+)
• Classic content-based relevance prospects (search)
– Items that has a specific keyword
• Social relevance prospects (browsing)
– Items bookmarked by a socially connected user
• Tag relevance prospects (browsing)
– Items tagged by a specific tag
• Personal relevance prospects (recommendation)
– Several different recommender engines
– Each engine offer one relevance prospect
50
Brusilovsky, P., Oh, J. S., López, C., Parra, D., and Jeng, W. (2017) Linking information and people in a social system for academic
conferences. New Review of Hypermedia and Multimedia 23 (2), 81-111.
47. SetFusion: User-Controlled Fusion
• Using set relevance visualization in
the familiar Venn diagram form
– One recommendation source = one set
• Allow controlled ranking
fusion
• Combine ranking with
annotation showing source(s)
of recommendation
55
Parra, D. and Brusilovsky, P. (2015) User-controllable personalization: A case study
with SetFusion. International Journal of Human-Computer Studies 78, 43–67.
48.
49. Set Fusion: Brief Results
• SetFusion provides strong engaging effect
– Number of engaged users, bookmarked talks,
explored talks doubled
– The effect is larger in UMAP “natural” settings
• SetFusion allows more efficient work
– Increases yield of bookmarks in relation to
overhead actions
• But only 3 dimensions of relevance with Venn!
• How to control for more than 3 dimensions?
57
50. RelevanceTuner: Control+Visualization
in a Hybrid Social Recommender
Tsai, Chun-Hua and Peter Brusilovsky (2018) Beyond the Ranked List: User-Driven Exploration and Diversification of Social
Recommendation. In 23rd International Conference on Intelligent User Interfaces, 239--50. Tokyo, Japan: ACM.
51. Transparent Profile-Result Match
Millecamp, M., Htun, N. N., Conati, C., and Verbert, K. (2020) What's in a User? Towards Personalising
Transparency for Music Recommender Interfaces. In: Proceedings of Proceedings of the 28th ACM Conference on
User Modeling, Adaptation and Personalization, Genoa, Italy, July 14–17, 2020, pp. 173-182.
52. Transparent Control Sliders
Kleemann, T. and Ziegler, J. (2020) Distribution sliders: visualizing data distributions in range selection sliders.
In: Proceedings of the Conference on Mensch und Computer (MuC '20), pp. 67–78.
56. Beyond the Ranking List:
Visualize + Explore
Present recommendations visually helping users to understand
how relevance mechanism work
64
57. Experiments with Visual
Exploration
• Adaptive Vibe (2006-2015)
– With Jae-Wook Ahn
• Relevance Explorer (2013-2016)
– With Katrien Verbert and Denis Parra
• Intersection Explorer (2017-2019)
– With Katrien Verbert, Karsten Seipp, Chen He, Denis
Parra, Bruno Cardoso, Gayane Sedrakyan, Francisco
Gutiérrez
• ScatterViz (2018)
– With Chun Hua Tsai
65
58. Adaptive VIBE: Exploring and
Controlling Adaptive Search
67
https://www.youtube.com/watch?v=Yt1fMEFlLVA&index=2&list=PLyCV9FE42dl7JG_i7m_kvwuYRpfwwJ4iY
Ahn,
Jaewook,
and
Peter
Brusilovsky.
2013.
'Adaptive
visualization
for
exploratory
information
retrieval',
Information
Processing
and
Management,
49:
1139–64.
59.
60. VIBE based query-profile fusion
User Profile Terms
Query Terms
Documents
Mixing user profile and query terms as VIBE POI
61. • User profile is added on the same playfield
as user query
• Topology is adaptive
• Mediate between profile (green POI) and
query (red POI) terms
• Browse documents free with control on
profile and query terms
Adaptive topology in VIBE
63. Some Study Results
• A sequence of user studies
– Search vs. VIBE vs. VIBE+NE
• Search -> VIBE -> VIBE+NE offers:
– Better visual separation of relevant documents (system)
– Supports better opening relevant documents (user)
• VIBE+NE supports more meanigful interaction
– No degradation found even with active visual UM
manipulation
– While over performance retained or increased
Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In:
Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA, March 29-
April 1, 2015, ACM, pp. 202-212
64. ScatterViz: Diversity-Focused
Exploration of Hybrid Recommendations
Tsai, Chun-Hua, and Peter Brusilovsky. 2018. "Beyond the Ranked List: User-Driven Exploration and Diversification of Social
Recommendation." In 23rd International Conference on Intelligent User Interfaces, 239--50. Tokyo, Japan: ACM.
Relevance sources as selectable axes
65. Relevance Explorer
• Context: multiple dimensions of relevance
– social - users, content - tags, recommender engines
• Using set relevance visualization
– One dimension of relevance = one set
• Agent metaphor to mix user- tag- and
engine-based relevance
– Users, tags, and recommender systems are shown as
agents collecting relevant talks
– Multiple-relevance match -> stronger evidence
78
66. TalkExplorer
• Recommendation engines are shown as agents in parallel to users and tags
• Uses Aduna clustermap library: http://www.aduna-software.com/
79
68. Evaluation
• Setup
– supervised user study
– 21 participants at UMAP 2012 and ACM Hypertext 2012 conferences
• Results
– The more aspects of relevance are fused, the more effective it is for
getting to relevant items. Especially effective are fusions across
relevance dimensions
– The more relevance prospects are merged, the better is the yield, the
easier is to find good items
– Dimensions of relevance are not equal
– ADUNA approach is challenging for beyond fusion of 3 aspects 84
Verbert, K., Parra-Santander, D., and Brusilovsky, P. (2016) Agents Vs. Users: Visual Recommendation of Research Talks
with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems 6 (2), Article No. 11
69. Intersection Explorer
• Based on ideas of
SetFusion and Talk
Explorer
• New approach for
scalable multi-set
visualization
85
Cardoso, Bruno, Gayane Sedrakyan, Francisco Gutiérrez, Denis Parra, Peter Brusilovsky, and Katrien Verbert. 2019. 'IntersectionExplorer, a multi-
perspective approach for exploring recommendations', International Journal of Human-Computer Studies, 121: 73-92.
72. Readings
• Ahn, Jae-wook, Peter Brusilovsky, Jonathan Grady, Daqing He, and Sue Yeon Syn (2007) Open user profiles
for adaptive news systems: help or harm? In the 16th international conference on World Wide Web, WWW '07, 11-20.
• Ahn, Jae-wook, Peter Brusilovsky, Daqing He, Jonathan Grady, and Qi Li.( 2008.) Personalized Web
Exploration with Task Models."In the 17th international conference on World Wide Web, WWW '08, 1-10. Beijing, China:.
• Ahn, J. and Brusilovsky, P. (2013) Adaptive visualization for exploratory information retrieval. Information Processing
and Management 49 (5), 1139–1164.
• Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In:
Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA,
March 29-April 1, 2015, ACM, pp. 202-212
• Verbert, K., Parra-Santander, D., and Brusilovsky, P. (2016) Agents Vs. Users: Visual Recommendation of
Research Talks with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems 6 (2), Article
No. 11
• Parra, D. and Brusilovsky, P. (2015) User-controllable personalization: A case study with SetFusion. International
Journal of Human-Computer Studies 78, 43–67.
• Cardoso, Bruno, Gayane Sedrakyan, Francisco Gutiérrez, Denis Parra, Peter Brusilovsky, and Katrien
Verbert (2019). IntersectionExplorer, a multi-perspective approach for exploring recommendations, International
Journal of Human-Computer Studies, 121: 73-92.
• Verbert, K., Parra-Santander, D., Brusilovsky, P., Cardoso, B., and Wongchokprasitti, C. (2017) Supporting
Conference Attendees with Visual Decision Making Interfaces. In: Companion of the 22nd International Conference on
Intelligent User Interfaces (IUI '17), Limassol, Cyprus, ACM.
• Tsai, Chun-Hua and Peter Brusilovsky (2018) Beyond the Ranked List: User-Driven Exploration and Diversification
of Social Recommendation. In 23rd International Conference on Intelligent User Interfaces, 239--50. Tokyo, Japan: ACM.
• Rahdari, B., Brusilovsky, P., and Babichenko, D. (2020) Personalizing Information Exploration with an Open User
Model. In: Proceedings of 31st ACM Conference on Hypertext and Social Media, July 13-15, 2020, ACM, pp. 167-176.
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