The document describes a PhD dissertation on linked data-based recommender systems. It presents an AlLied framework for executing and analyzing recommendation algorithms based on linked data. The framework includes implementations of graph-based and machine learning algorithms. An evaluation compares the performance of different graph-based algorithms using a user study on film recommendations. The results show that algorithms combining traversal and hierarchical approaches have the best balance of accuracy and novelty.
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Phd thesis final presentation
1. Recommender Systems
based on
Linked Data
PhD Dissertation
Cristhian Figueroa
PhD Maurizio Morisio PhD Juan Carlos Corrales
Politecnico di Torino Universidad del Cauca
March, 2017
2. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
3. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
4. Linked Data based Recommender Systems
RS: Software tools providing suggestions of resources to be of interest to a user
4
Recommender Systems (RS)
Preprocessing
Recommender
Algorithm Ranking
Input data
Recommended
resources
Background
data
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Large heterogeneous data on the Web
-> Need for an automatic filtering tool
5. Linked Data based Recommender Systems
Approach Technique
Content-based Keywords, pixels, disk space etc.
Collaborative Filtering Similar users’ preferences
Knowledge-based Matches user requirements - resources
features
Hybrid Combines two or more approaches
5
Traditional RS approaches
Problems of traditional RS:
TScarcity: few ratings with regard to the total number of
resources
TCold start: new user/resource without ratings
TNo standard data formats – low compatibility
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
6. Linked Data based Recommender Systems
• Linked Data (LD): publishing and linking
structured data on the Web
• 1146 datasets (Jan 2017) [http://lod-cloud.net]
• LD-based RS: LD -> knowledge about
resources/users for recommendations
from various domains.
6
Linked Data based RS
Problems of traditional RS:
TScarcity
TCold start
TNo standard data formats
Linked Data based RS:
RDo not require ratings data or
user profile
RStandardized access to data
RMulti-domain knowledge
R Richer resource representation
R Semantic Analysis
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
7. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
8. Linked Data based Recommender Systems
• Kitchenham guidelines [kitchenham and Charters, 2007]
• Main results:
1. Classification of Algorithms for LD based RS
2. Summary of the issues in current LD based RS
8
Systematic Literature Review (SLR)
Published on CPE Journal: “A Systematic Review of Linked Data-based
Recommender Systems”
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
9. Linked Data based Recommender Systems 9
SLR: 1. Algorithms
Classification of algorithms for Linked Data-based RS
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
42,9%
20,0%
14,3%
8,6%
14,3%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Percentageofstudies
Graph-based
Machine Learning
Memory based
Probabilistic
Others
10. Linked Data based Recommender Systems 10
SLR: 1. Algorithms
Type Algorithms Advantages
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Graph-based
• Semantic exploration
- Dbrec[Passant,2010]
- HyProximity[Stankovic et al,2012]
- Page rank[Nguyen et al,2015]
- VSM[Khrouf et al,2013]
• Path based:
- Spreading activation[Hajra et al,2014]
- Random walk[Cantador et al,2011]
- Vertex discovery[Strobin et al,2014]
• Serendipitous Rec.
• Explanations
• Domain independent
• Hierarchical data
Machine
Learning
(ML)
• Supervised - Classification
- kNN[Ristoski et al,2013]
- decision trees[Ostuni et al,2013]
- RF[Musto et al,2014]
- SVM[Kushwaha et al,2014]
- Naïve bayes[Schmachtenberg et al,2014]
- Bayesian classifier [Rabello Lopes et al,2014]
• Unsupervised - Clustering
- k-means[Manoj Kumar et al,2015]
- Fuzzy-C means, SOM, PCA[Ostuni etal,2014]
• Many algorithms already
developed.
• Large datasets.
• Improve results with
experience
11. Linked Data based Recommender Systems 11
SLR: 2. Current gaps of LD based RS
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Topic Issues
Datasets
• Local copy of LD datasets
• Restricted access
Manual
Operations
• Curated datasets – manual review
• Selection of concepts for a domain
Algorithms
• Graph-based:
- High cost for exploiting semantic features
• ML:
- No intrinsic semantic structure of the LD
- Ratings and user’s profile information – cold start
problem
12. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
13. Linked Data based Recommender Systems
• How to recommend resources considering the knowledge
available on the web of data, analyzing their implicit
relationships?
13
Research Question
LD cloud
Preprocessing
Recommender
Algorithm Ranking
Input data
Recommended
resources
Background
data
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
14. Linked Data based Recommender Systems
1. Recommendation -> semantic relationships from LD
2. Layered framework -> execute and test algorithms to
create novel RS.
3. Graph-based algorithms in RS: more reliable ->
intrinsic structure of the LD
4. ML algorithms in RS -> do not require the user’s
profile information
14
Hypothesis
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
15. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
16. Linked Data based Recommender Systems
• A framework for executing and analyzing
recommendation algorithms based on LD: AlLied
• A selection of algorithms for RS -> knowledge of LD
• Implementations:
• Graph-based
• ML
16
Approach
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
17. Linked Data based Recommender Systems 17
Conceptual architectures
Recommender
System
Management
User interface
and applications
Common Layers
Knowledge Base
Management
Bottom Layer:
structured data
with user defined
vocabularies
Middle Layer:
semantic web
core
Top Layer:
user interface for
applications
Semantic Web Stack: (Horrocks et. Al, 2005)
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
18. Linked Data based Recommender Systems
Conceptual architectures
Recommender
System
Management
User interface
and applications
Common Layers
Knowledge Base
Management
Conceptual architecture for applications on the
web of data: (Heitman et. Al, 2012)
RDF store
Graph
access
Data
homogenization
service
Data discovery
service
Graph query
language
Graph-based
navigation
interface
Application
logic
Structured data
authoring
interface
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
18
19. Linked Data based Recommender Systems 19
AlLied Framework
Steps of the
recommendation process
Resource generation
Rec1 Rec2 Recn
…
Results grouping
Rank1 Rank2
…
Results ranking
Rankn
Presentation
Recommender
System
Management
User interface and
applications
AlLied framework
Conceptual
architectures
Knowledge Base Knowledge Base
Management
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Candidate
Resource
generation
ranking
Results grouping
Results
presentation
Preprocessing
20. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
21. Linked Data based Recommender Systems
Graph-based Implementation
Hierarchical Traversal
Dynamic
ReDyAl
HyProximity LDSD Hybrid
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Remote
Dataset
(Dbpedia)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
21
22. Linked Data based Recommender Systems
Graph-based Implementation
Hierarchical Traversal
Dynamic
ReDyAl
HyProximity LDSD Hybrid
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Remote
Dataset
DBpedia
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
22
Preprocessing
23. Linked Data based Recommender Systems
• Dbpedia:
• Hierarchical schemas:
• Wikipedia categories (SKOS)
• Traversal relationships:
• Direct and indirect relationships
23
Graph-based: knowledge base
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
24. Linked Data based Recommender Systems
Graph-based Implementation
Hierarchical Traversal
Dynamic
ReDyAl
HyProximity LDSD Hybrid
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Remote
Dataset
(Dbpedia)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
24
Candidate
Resource
generation
Preprocessing
25. Linked Data based Recommender Systems
• Traversal Generator:
• Direct and indirect links - properties
• Hierarchical Generator:
• Distance in the category tree
Graph-based: generation
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
25
The dark knight Man of steel
American superhero films
Batman film
Batman begins
dbpedia-owl:director
Christopher Nolan
dbpedia-owl:director
Superhero films
Superhero comedy films
Megamind
Initial Resource
The mask
Warner bros
dbpedia-owl:distributor
dbpedia-owl:distributor
dbpedia-owl:distributor
1
2
3
3
26. Linked Data based Recommender Systems
Hierarchical Generator
Traversal Generator
26
Graph-based: generation: ReDyAl algorithm
Initial
Resource
Traversal
Generation
CR>=
MinRes
LinksIR >=
MinLinks
Ranking
Create category
graph
CR>=
MinRes
Update
category graph
CurrLevel >=
MaxDist
Y Y
Y
Y
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Hierarchical
Generation
currLevel++
27. Linked Data based Recommender Systems
Graph-based Implementation
Hierarchical Traversal
Dynamic
ReDyAl
HyProximity LDSD Hybrid
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Remote
Dataset
(Dbpedia)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
27
Candidate
Resource
generation
ranking
Preprocessing
28. Linked Data based Recommender Systems
• HyProximity: [Stankovic., et al, 2011]
ℎ𝑦𝑃 𝑐%, 𝑐' =
𝑝 𝑐%, 𝑐'
𝑑 𝑐%, 𝑐'
• Hierarchical measures: categories
𝑝 𝑐%, 𝑐' = 1 −
𝑙𝑜𝑔(ℎ𝑦𝑝𝑜 𝐶 + 1)
𝑙𝑜𝑔 𝐶
• Traversal measures: properties
𝑝 𝑐%, 𝑐' = −𝑙𝑜𝑔
𝑛
𝑀
• Linked Data Semantic Distance: [Passant, 2010]
• Traversal distance: input/output direct and indirect links
𝐿𝐷𝑆𝐷 𝑐%, 𝑐' =
1
1 + 𝐶𝑑9:; + 𝐶𝑑<= +𝐶𝑖9:; +𝐶𝑖<=
• Hybrid:
• Traversal + hierarchical
28
Graph-based: ranking
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
distance
weighting
function
29. Linked Data based Recommender Systems
Graph-based Implementation
Hierarchical Traversal
Dynamic
ReDyAl
HyProximity LDSD Hybrid
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Remote
Dataset
(Dbpedia)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
29
Candidate
Resource
generation
ranking
Results grouping
Preprocessing
30. Linked Data based Recommender Systems
• Meaningful broader category
• Less common ancestor
30
Graph-based: results categorizer
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Batman
begins
American
Superhero films
Batman Films
Man of
steel
Christopher
Nolan
Superhero films
Superhero
comedy films
Megamind
English
directors
The dark
knight
Neo noir
films
The mask
Film noir
Crime
films
Films genres
Warner
bros
American
film
studios
Entertainment
31. Linked Data based Recommender Systems
Graph-based Implementation
Hierarchical Traversal
Dynamic
ReDyAl
HyProximity LDSD Hybrid
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Remote
Dataset
(Dbpedia)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
31
Candidate
Resource
generation
ranking
Results grouping
Results
presentation
Preprocessing
32. Linked Data based Recommender Systems
• User study:
• Domain: films
• 109 students (PoliTo and
Unicauca)
• 45 queries (top films of IMDb)
• Executed AlLied algorithms
• Test List: merging top 10 films for
each query and each algorithm.
• Only top 20 results of the merged
list
• Measures:
• RMSE: the square root of the mean of
the square of all of the error.
• Novelty: relevant recommended
resources not known by the total
number of resources
32
Graph-based: experimental setup
Benchmarking website:
http://natasha.polito.it/RSEvaluation/
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
33. Linked Data based Recommender Systems
Graph-based: experimentation
Bad performers
High novelty
Sweet spot
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Traversal + LDSD
34. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
35. Linked Data based Recommender Systems
ML implementation
Mixed Euclidean
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Local Dataset
(LODMatrix)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Clustering
K-Means
Classification
kNN
Bregman
Distances
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
35
36. Linked Data based Recommender Systems
ML implementation
Mixed Euclidean
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Local Dataset
LODMatrix
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Clustering
K-Means
Classification
kNN
Bregman
Distances
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
36
Preprocessing
Steps of the
recommendation
process
37. Linked Data based Recommender Systems
• LODMatrix
• Creation of a dataset tailored for ML
T Need to know the application domain -> obtain only relevant data from that
domain
• Films from DBpedia
• Data mining: quality of results can only be good as the input data [Paulheim.,
et al, 2012]
• FDQ-KDT framework: [Corrales et al, 2015]
• Address poor quality data in knowledge discover tasks.
37
ML: Knowledge base
Data extraction
• Attribute selection
• Matrix generation
Data quality assessment
• Outliers detection
• LOF
• Tuckey’s method
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
38. Linked Data based Recommender Systems
• LODMatrix
38
ML: Knowledge base
Data extraction
• Attribute selection
• Matrix generation
Data quality assessment
• Outliers detection
• LOF
• Tuckey’s method
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
FDQ-KDT framework: [Corrales et al, 2015]
39. Linked Data based Recommender Systems
• LODMatrix:
• Attribute Selection:
• Common features in state of the art RS: Discovery hub, LinkedMDB,
SemMovieRec, Cinemappy, etc.
• Matrix Generation:
• All possible combinations of the attributes for each film.
• 101000 films approx. (about 3 million instances)
39
ML: Knowledge base
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
40. Linked Data based Recommender Systems
• LODMatrix
40
ML: Knowledge base
Data extraction
• Attribute selection
• Matrix generation
Data quality assessment
• Outliers detection
• LOF
• Tuckey’s method
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
FDQ-KDT framework: [Corrales et al, 2015]
41. Linked Data based Recommender Systems
• LODMatrix: Data quality assessment:
• DBpedia -> lot of missing and wrong data (outliers).
• Local Outlier Factor:
• local density given by the kNN
• Tuckey’s method:
• 1.5 Interquartile range
• LODMatrix was depurated -> fixing data containing
outliers
• Automatic: Free web services -> fix erroneous data:
• The Movie DB
• OMDb
• Manual fixing : tedious task.
41
ML: Knowledge base
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
42. Linked Data based Recommender Systems
ML implementation
Mixed Euclidean
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Local Dataset
(LODMatrix)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Clustering
K-Means
Classification
kNN
Bregman
Distances
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
42
Candidate
Resource
generation
Preprocessing
43. Linked Data based Recommender Systems
ML: Generation
Extract films in the
same cluster as the
query film
Classification
Model
IR
Query film
Is it a new
film?
LODMatrix
with cluster
data
Y Inferring
cluster
Candidate
Resources
N
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
43
• Generation phase:
• Extracts films belonging to the
same cluster as the IR -> reduces
the search space
• For new films uses the classification
algorithm
44. Linked Data based Recommender Systems
ML: Generation
LODMatrix
Clustering Classification
Classification
Model
Training
Data
Cluster
model
Add clusters to
lodmatrix
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
44
• Training phase:
• Clustering:
• Discover a set of clusters –> similar features
• Classification:
• New films (not in the LODMatrix)
• Predicts the cluster based on attributes and a
trained model.
45. Linked Data based Recommender Systems
• Clustering:
• K-means: most popular algorithm in RS based on LD:
R Easy to use, fast algorithm and good quality clusters
T k value selection
• Selection of k value empirically: 2 – 100 clusters
• Distance
• Distribution
• Density
• K = 55 -> best values
ML: Generation
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
45
46. Linked Data based Recommender Systems
• Classification: Algorithms selected: most commonly used for
RS in the state of the art.
ML: Generation
13,22%
16,83%
75,67% 75,69%
0%
10%
20%
30%
40%
50%
60%
70%
80%
kNN Naive Bayes Decision Tree Random Forest
Classification Error (%)
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
46
47. Linked Data based Recommender Systems
ML implementation
Mixed Euclidean
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Local Dataset
(LODMatrix)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Clustering
K-Means
Classification
kNN
Bregman
Distances
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
47
Candidate
Resource
generation
ranking
Preprocessing
48. Linked Data based Recommender Systems
• Mixed Euclidean:
R heterogeneous distance measure
• Bregman Divergences:
• Distance consistent with the NDCG [Acharyya et al,2012]
• Itakura Saito, logarithmic loss, logistic loss, squared Euclidean and squared loss.
R Easy to implement
R Heterogeneous data
R Execution time
48
ML: Ranking
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
49. Linked Data based Recommender Systems
ML implementation
Mixed Euclidean
Results Categorizer
Visual
Interface
REST
Interface
Tree Graph Log
Query
Controller
Local Dataset
(LODMatrix)
Presentation
Grouping
Generation
Ranking
Knowledge Base
Management
Recommender System Management
Clustering
K-Means
Classification
kNN
Bregman
Distances
Steps of the
recommendation
process
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
49
Candidate
Resource
generation
ranking
Results grouping
Results
presentation
Preprocessing
50. Linked Data based Recommender Systems
• User study:
• Used the lists generated in the user study for the graph based
algorithms
50
ML: Experimental Setup
AlLied
• Measures:
• Precision: fraction of retrieved resources that are
relevant
• Recall: fraction of relevant instances that are
retrieved
• F-Measure: harmonic mean between precision
and recall
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
• Gold standard:
• IMDb gold standard for films.
• Top 10 films from IMDb for
each query film
51. Linked Data based Recommender Systems 51
ML: User Study
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
Mixed
Euclidean
Generalized
Divergence
Itakura Saito Logarithmic
Loss
Logistic Loss Squared
Euclidean
Squared Loss
Precision Recall F-Measure
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
52. Linked Data based Recommender Systems 52
ML: Gold standard study
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
Mixed Euclidean Generalized
Divergence
Itakura Saito Logarithmic Loss Squared
Euclidean
Squared Loss
Precision Recall F-Measure
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
53. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
54. Linked Data based Recommender Systems 54
Comparative evaluation: User Study
43,2%
20,0%
35,7%
22,0%
18,7%
9,9%
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
30,0%
35,0%
40,0%
45,0%
50,0%
ReDyAl - Hybrid Ranker ML - Mixed Euclidean
Precision Recall F-Measure
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
55. Linked Data based Recommender Systems 55
Comparative evaluation: Gold standard
34,2%
24,2%
28,5%
20,1%
15,5%
11,0%
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
30,0%
35,0%
40,0%
45,0%
50,0%
ReDyAl - Hybrid Ranker ML - Mixed Euclidean
Precision Recall F-Measure
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
56. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
57. Linked Data based Recommender Systems
• First SLR in LD-based RS
• Allied: framework for testing and executing LD based RS
• Implementation: graph-based and ML algorithms
• ReDyAl: a dynamic algorithm for LD based RS
• User and gold standard studies to assess accuracy
• 14 research papers published during the PhD.
• Directly related with the research topic:
• 4 JCR journal papers
• 2 conference papers (LNCS -Springer)
• Others: 8 papers
Contributions
57
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
58. Linked Data based Recommender Systems
1. Motivation
2. State of the art
3. Problem definition
4. AlLied Framework
I. Graph-based
II. Machine learning
5. Comparative evaluation
6. Contributions
7. Conclusions and future works
Outline
59. Linked Data based Recommender Systems
• AlLied
• Dividing the recommendation process in meaningful
phases help to:
R Test and use different types of algorithms for each phase
R Modular, extensible and reusable
• Use of Semantic web standards
R New RS with different configurations of algorithms
R Reproducibility of results
• ReDyAl:
• Accurate and Novel recommendations
Conclusions
59
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
60. Linked Data based Recommender Systems
• ML and graph based algorithms for LD-based RS:
• Graph-based implementation:
• Advantage of the intrinsic relationships among data – graph
structure of LD:
R Better accuracy
T Cannot deal with huge datasets
• ML implementation:
R Can recommend resources without user profile information
R Deal with large amounts of data.
R Classification -> new resources
T Limited application domain
T Datasets need to be adapted for ML
• ML can be combined with graph-based algorithms to improve
results.
Conclusions
60
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
61. Linked Data based Recommender Systems
• TellMeFirst:
• Integration of ReDyAl and multisource
• “Semantic Annotation and Classification in Practice” published on “IEEE ITPro Magazine”
• Mobile application for films recommendation:
• “ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data” presented on CBRecSys
2016
• Touristic recommender with Smart Spaces:
• Associate touristic attraction with concepts of LD
• “Linked Data-Driven Smart Spaces” presented on ruSMART 2014
• Multimodal Search of Business Processes:
• Integration of the hierarchical generator -> hierarchical index for a
BP repository.
• “Improving Business Process Retrieval Using Categorization and Multimodal Search” published
on “Knowledge-based Systems” journal
Use cases
61
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
62. Linked Data based Recommender Systems
• More types of algorithms for the AlLied
framework
• Explanations of recommendations based on the
knowledge of LD
• Evaluation of accuracy and performance for
algorithms.
• Determine best algorithms for each component of the
AlLied framework.
• Personalized recommendations with LD
Future works
62
Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
63. Linked Data based Recommender Systems 63
Acknowledgements
• COLCIENCIAS
• PhD grant
• Politecnico di Torino
• SoftEng group
• Universidad del Cauca
• VRI
• Telematics Engineering Group
64. Thanks!!!
Recommender Systems based on
Linked Data
Cristhian Figueroa
cristhian.figueroa@polito.it
cfigmart@unicauca.edu.co
PhD Maurizio Morisio PhD Juan Carlos Corrales
Politecnico di Torino Universidad del Cauca