Downloaded 111 times
![Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion of RDF triples from hundreds of data sources;
• Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ]
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China](https://image.slidesharecdn.com/sprankrecsys2013-131017103707-phpapp01/75/Top-N-Recommendations-from-Implicit-Feedback-leveraging-Linked-Open-Data-3-2048.jpg)
![Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion of RDF triples from hundreds of data sources;
• Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ]
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China](https://image.slidesharecdn.com/sprankrecsys2013-131017103707-phpapp01/75/Top-N-Recommendations-from-Implicit-Feedback-leveraging-Linked-Open-Data-4-2048.jpg)
![BagBoo
BagBoo: a scalable hybrid bagging-the-boosting model
[D. Pavlov, A. Gorodilov, C. Brunk CIKM2010]
• Combination of Random Forest (Bagging) and Gradient Boosted
Regression Trees (Boosting)
• Combines the high accuracy of gradient boosting with the resistance
to overfitting of random forests
For b=1 to B:
Tb TR
fb learn GBRT from Tb
1 B
f fb
B b 1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China](https://image.slidesharecdn.com/sprankrecsys2013-131017103707-phpapp01/75/Top-N-Recommendations-from-Implicit-Feedback-leveraging-Linked-Open-Data-27-2048.jpg)
![Evaluation Methodology
• Top-N Item recommendation task
• Evaluation methodology similar to:
[Cremonesi, Koren and Turrin, RecSys 2010]
• Evaluation with different user profile size:
given 5
given 10
User
profile
5
User profile
Test Set
10
……
given All
User profile
Test Set
10
Test Set
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China](https://image.slidesharecdn.com/sprankrecsys2013-131017103707-phpapp01/75/Top-N-Recommendations-from-Implicit-Feedback-leveraging-Linked-Open-Data-28-2048.jpg)
Uploaded byVito Ostuni
Top-N Recommendations from Implicit Feedback leveraging Linked Open Data
The document describes a new approach called SPrank for top-N recommendations from implicit feedback using linked open data. SPrank analyzes relationships between user preferences and items through path-based features extracted from a knowledge graph. A learning to rank method is used to learn the ranking function from these features. Experimental results on movie and music datasets mapped to DBpedia show SPrank outperforms other recommendation techniques, particularly with smaller user profiles.
More Related Content
![[Final]collaborative filtering and recommender systems](https://cdn.slidesharecdn.com/ss_thumbnails/finalcollaborativefilteringandrecommendersystems-141103044224-conversion-gate01-thumbnail.jpg?width=640&height=640&fit=bounds)
Similar to Top-N Recommendations from Implicit Feedback leveraging Linked Open Data
![[UPDATE] Udacity webinar on Recommendation Systems](https://cdn.slidesharecdn.com/ss_thumbnails/udacitywebinar-190716143828-thumbnail.jpg?width=640&height=640&fit=bounds)
Top-N Recommendations from Implicit Feedback leveraging Linked Open Data
- 1. Top-N Recommendations from ImplicitFeedback leveraging Linked Open Data Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi ostuni@deemail.poliba.it, t.dinoia@poliba.it, disciascio@poliba.it, mirizzi@deemail.poliba.it Polytechnic University of Bari - Bari (ITALY) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 2. Outline Introduction andmotivation SPrank: Semantic Path-based ranking Data model and Problem formulation Path-based features Learning the ranking function Experimental Evaluation Contributions and Conclusion RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 3. Linked Open Data •Initiative for publishing and connecting data on the Web using Semantic Web technologies; • >30 billion of RDF triples from hundreds of data sources; • Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ] RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 4. Linked Open Data •Initiative for publishing and connecting data on the Web using Semantic Web technologies; • >30 billion of RDF triples from hundreds of data sources; • Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ] RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 5. Hong Kong inDBpedia db:Hong_Kong db:thumbnail subject predicate object 8134 triples RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 6. Hong Kong inDBpedia db:Hong_Kong db:thumbnail Skyscrapers over 350 meters in Hong Kong? select * where { ?s dbpedia-owl:location <http://dbpedia.org/resource/Hong_Kong>. ?s dcterms:subject category:Skyscrapers_over_350_meters. } RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 7. Hong Kong inDBpedia db:Hong_Kong db:thumbnail db:location db:International_Commerce_centre db:thumbnail db:Central_Plaza_(Hong_Kong) dcterms:subject db:thumbnail db:category:Skyscrapers_over_350_meters) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 8. Motivation Traditional Ontological/Semantic RecommenderSystems: • make use of limited domain ontologies; • rely on explicit feedback data; • address the rating prediction task. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 9. Motivation Traditional Ontological/Semantic RecommenderSystems: • make use of limited domain ontologies; • rely on explicit feedback data; • address the rating prediction task. But… • a lot of structured semantic data on the Web; • Implicit feedback are easier to collect; • Top-N Recommendations is a more realistic task. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 10. Motivation Traditional Ontological/Semantic RecommenderSystems: • make use of limited domain ontologies; • rely on explicit feedback data; • address the rating prediction task. But… • a lot of structured semantic data on the Web; • Implicit feedback are easier to collect; • Top-N Recommendations is a more realistic task. Challenge: • compute Top-N Item Recommendations from implicit feedback exploiting the Web of Data. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 11. Our approach • Usageof structured semantic data freely available on the Web (Linked Open Data) to describe items DBpedia ontology RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 12. Our approach • Analysisof complex relations between the user preferences and the target item (extraction of path-based features) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 13. Our approach • Analysisof complex relations between the user preferences and the target item (extraction of path-based features) • Formalization of the Top-N Item recommendation problem from implicit feedback in a Learning To Rank setting RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 14. Data model Implicit FeedbackMatrix ^ S I1 i2 i3 i4 u1 1 1 0 0 u2 1 0 1 0 u3 0 1 1 0 u4 0 1 0 Knowledge Graph 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 15. Data model Implicit FeedbackMatrix ^ S I1 i2 i3 i4 u1 1 1 0 0 u2 1 0 1 0 u3 0 1 1 0 u4 0 1 0 Knowledge Graph 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 16. Data model Implicit FeedbackMatrix ^ S I1 i2 i3 i4 u1 1 1 0 0 u2 1 0 1 0 u3 0 1 1 0 u4 0 1 0 Knowledge Graph 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 17. Problem formulation u ^ u ^ I {i I | s ui 1} Set of relevant items for u I {i I | s ui 0} Set of irrelevant items for u Iu * Iu Sample of irrelevant items for u xui Feature vector D ^ xui , s ui i ( I u I u * ) TR u Training Set RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 18. Path-based features path acyclicsequence of relations ( s , .. rl , .. rL ) u3 s i2 p2 e1 p1 i1 xui ( j ) (s, p2 , p1) # pathui ( j ) D # path d 1 ui (d ) Frequency of pathj in the sub-graph related to u ad i • The more the paths, the more the item is relevant. • Different paths have different meaning. • Not all types of paths are relevant. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 19. Path-based features i1 xu3i1 ? e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
- 20. Path-based features path1 (s,s, s) : 1 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
- 21. Path-based features path1 (s,s, s) : 2 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
- 22. Path-based features path1 (s,s, s) : 2 path2 (s, p2, p1) : 1 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
- 23. Path-based features path1 (s,s, s) : 2 path2 (s, p2, p1) : 2 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
- 24. Path-based features path1 (s,s, s) : 2 path2 (s, p2, p1) : 2 path3 (s, p2, p3, p1) : 1 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
- 25. Path-based features path1 (s,s, s) : 2 path2 (s, p2, p1) : 2 path3 (s, p2, p3, p1) : 1 2 xu3i1 (1) 5 2 xu3i1 (2) 5 1 xu3i1 (3) 5 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
- 26. Learning the rankingfunction Point-wise Learning To Rank Learn a prediction function f : D ^ s.t. f ( xui ) sui Assumption: if f is accurate, then the ranking induced by f should be close to the desired ranking • Simplest LTR technique • Very effective in practice (Yahoo! Learning to Rank Challenge best solution was extremely randomized trees in a standard regression setting) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 27. BagBoo BagBoo: a scalablehybrid bagging-the-boosting model [D. Pavlov, A. Gorodilov, C. Brunk CIKM2010] • Combination of Random Forest (Bagging) and Gradient Boosted Regression Trees (Boosting) • Combines the high accuracy of gradient boosting with the resistance to overfitting of random forests For b=1 to B: Tb TR fb learn GBRT from Tb 1 B f fb B b 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 28. Evaluation Methodology • Top-NItem recommendation task • Evaluation methodology similar to: [Cremonesi, Koren and Turrin, RecSys 2010] • Evaluation with different user profile size: given 5 given 10 User profile 5 User profile Test Set 10 …… given All User profile Test Set 10 Test Set RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 29. Datasets Subset of Movielensmapped to DBpedia 3,792 users 2,795 movies 104,351 entities Subset of Last.fm mapped to DBpedia 852 users 6,256 artists 150,925 entities Mappings http://sisinflab.poliba.it/mappingdatasets2dbpedia.zip RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 30. Evaluation of differentranking functions Movielens 0,6 0,5 recall@5 0,4 BagBoo 0,3 GBRT Sum 0,2 0,1 0 given 5 given 10 given 20 given 30 given 50 given All user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 31. Evaluation of differentranking functions Last.fm 0,6 0,5 recall@5 0,4 BagBoo 0,3 GBRT Sum 0,2 0,1 0 given 5 given 10 given 20 given All user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 32. Comparative approaches MyMediaLite • BPRMF,Bayesian Personalized Ranking for Matrix Factorization • BPRLin, Linear Model optimized for BPR (Hybrid alg.) • SLIM, Sparse Linear Methods for Top-N Recommender Systems • SMRMF, Soft Margin Ranking Matrix Factorization RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 33. Comparison with otherapproaches Movielens 0,6 0,5 recall@5 0,4 SPrank BPRMF 0,3 SLIM BPRLin 0,2 SMRMF 0,1 0 given 5 given 10 given 20 given 30 given 50 user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China given All
- 34. Comparison with otherapproaches Last.fm 0,6 0,5 recall@5 0,4 SPrank BPRMF 0,3 SLIM BPRLin 0,2 SMRMF 0,1 0 given 5 given 10 given 20 given All user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 35. Contributions SPrank: Semantic Path-basedranking Combination of semantic item descriptions from the Web of Data and implicit feedback Mining of the semantic graph using path-based features Learning To Rank setting Future Work: Deeper analysis of the path-based features Usage of other Learning To Rank approaches RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
- 36. Q&A A Little SemanticsGoes a Long Way. Hendler Hypothesis RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China