Combining content analytics and activity tracking to mine user interests and enable knowledge discovery - UMAP PALE 2016 - Andrii Vozniuk, Maria Rodriguez-Triana, Adrian Holzer, Denis Gillet
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The paper was presented at UMAP PALE 2016: goo.gl/5cJsSK Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environment. Due to the number of content items stored in such repositories potentially reaching millions or more, and quickly increasing, for the user it can be challenging to find relevant content by browsing or relying on the available search engine. In this paper, we propose to address the problem by providing content and people recommendations based on user interests, enabling relevant knowledge discovery. To build a user interests profile automatically, we propose an approach combining content analytics and activity tracking. We have implemented the recommender system in Graasp, a knowledge management system employed in educational and humanitarian domains. The conducted preliminary evaluation demonstrated an ability of the approach to identify interests relevant to the user and to recommend relevant content.
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Combining content analytics and activity tracking to mine user interests and enable knowledge discovery - UMAP PALE 2016 - Andrii Vozniuk, Maria Rodriguez-Triana, Adrian Holzer, Denis Gillet
- 1. Combining Content Analytics and Activity Tracking to Identify User Interests and Enable Knowledge Discovery Andrii Vozniuk, María Jesús Rodríguez-Triana, Adrian Holzer, Denis Gillet The copyright of images belongs to their authors. I will remove them on demand. Contact me at andrii.vozniuk@epfl.ch UMAP PALE, Halifax, July 2016 Paper: https://goo.gl/5cJsSK
- 2. REACT - EPFL - Lausanne
- 4. SpeakUp is a co-located social media to improve audience interaction speakup.info
- 5. graasp.euGraasp a social media used as a personal and collaborative learning environment
- 6. Inquiry Learning Spaces Teachers construct courses for their students by finding and structuring relevant content
- 8. Teachers would like to benefit from relevant content uploaded by others Students would like to get relevant content even when not included
- 9. How to suggest relevant content to users ?& keep them in control
- 10. Approach
- 11. “Learner-content interaction is a defining characteristic of education …” M. G. Moore. Editorial: Three types of interaction.The American Journal of Distance Education, 3(2):1–6, 1989. ”… it is the process of intellectually interacting with content that results in changes in the learner’s understanding, the learner’s perspective, or the cognitive structures of the learner’s mind”
- 12. Recommenders for Learning A review of 82 recommenders for learning [Drachsler et al 2015] Discovering by the instructors relevant learning resources used by students when learning, that are not part of the materials provided by the instructor [Zaldivar et. al. 2011] • Considered present terms to describe the content • TF-IDF based on terms from the content • Looked at one type of interaction (visit) • No possibility to adjust recommendations by the user Personalized recommendations of relevant knowledge assets based on user interactions with content [El Helou et. al. 2010] • Built user-content graph based on interactions • Used modified PageRank to get relevant items • Considered multiple types of interactions • Did not look inside of the content • No possibility to adjust recommendations by the user No explicit identification of interests. No control over them.
- 14. Extracting Concepts Extracted Text Content Items on platform Binary Text File .pdf .docx Image with text .png .jpg .tiff Image Audio Video Content Extraction Plain Text File Optical Character Recognition Speech-To- Text Visual Image Recognition Visual Video Recognition Content Analysis Content and Concepts Indexing Identified Concepts Indexed Identified Concepts and Text Content Recommender System
- 15. Pdf Report Powerpoint Presentation Image with Text Youtube Video Σw*UA *DC accessed rated commented downloaded Education Educational psychology Knowledge Learning Knowledge Management Human-Computer Interaction Interdisciplinarity Academia Systems thinking Scientific method Educational technology Virtual learning environment User Identified Concepts (DC) Identified User Concepts (UC) Tracked Activities (UA) Education Educational psychology Knowledge Learning Knowledge Management Systems thinking Scientific method Educational technology Virtual learning environment Learning Knowledge Management Human-Computer Interaction Interdisciplinarity Education Educational psychology Academia Building Interests Profile
- 16. Providing Recommendations Step 2. Use vector cosine similarity for scoring and ranking Step 1. Compute TF-IDF for each term in the vectors Step 0. Represent each content item concepts using the document vector model
- 18. Graasp AlchemyAPI for concept extraction ActivityStreams / xAPI for Interaction Tracking ElasticSearch for storage and recommendations Open-source tools for text extraction
- 20. Preliminary Evaluation • Six pre-service teachers, participants of a workshop on inquiry-based learning • They were newly registered users (no interaction data) • Interacted for 2 hours • Survey from three parts 1. General disposition towards the interests identification and the interests-based recommender 2. System Usability Scale for the solution 3. Recommender Precision
- 21. Evaluation Outcomes Complete results: https://goo.gl/Wes6uP Would like to edit interests Concerned about privacy We use a 5-point Likert Scale
- 22. Evaluation Outcomes Was easy to use Easy to get started
- 23. Evaluation Outcomes Misidentified concepts in popular content can push up irrelevant concepts Two groups: relevant and irrelevant interests Two groups: relevant and irrelevant suggestions
- 24. Conclusions • Proposed a general and scalable approach deployable in systems where content and interactions are available • Allows users to modify the interests • Implementation in a real system, can be used as a guideline • Preliminary evaluation in an authentic setting
- 25. Future Work • Address misidentified concepts-related issues • Learn optimal action weights • Incorporate concept relevance score into similarity • Substantial Evaluation • Run a bigger scale evaluation • Check not only precision, but as well recall • Compare to existing approaches. Dataset?