A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Knowledge-Based Systems

Volumn 21, Issue 4, 2008, Pages 305-320

  Blanco Fernández, Yolanda ^a   Pazos Arias, José J ^a   Gil Solla, Alberto ^a   Ramos Cabrer, Manuel ^a   López Nores, Martín ^a   García Duque, Jorge ^a   Fernández Vilas, Ana ^a   Díaz Redondo, Rebeca P ^a   Bermejo Muñoz, Jesús ^b  

^a UNIVERSITY OF VIGO   (Spain)

^b Telvent   (Spain)

Author keywords

Content based filtering; Ontologies; Recommender systems; Semantic reasoning; Semantic Web

Indexed keywords

DIGITAL TELEVISION; E-LEARNING; INFORMATION RETRIEVAL; KNOWLEDGE BASED SYSTEMS; USER INTERFACES;

CONTENT-BASED FILTERING; RECOMMENDER SYSTEMS; SEMANTIC REASONING;

SEMANTIC WEB;

EID: 42949145354     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2007.07.004     Document Type: Article

References (44)

1. K. Anyanwu, A. Sheth, ρ-Queries: enabling querying for semantic associations on the semantic web, in: Proceedings of 13th International World Wide Web Conference, 2003, pp. 117-127.
2. Ardissono L., Gena C., Torasso P., Bellifemine F., Difino A., and Negro B. User modeling and recommendation techniques for personalized electronic program guides. Personalized Digital Television: Targeting Programs to Individual Viewers (2004), Kluwer Academics Publishers pp. 3-26
3. Ardissono L., Kobsa A., and Maybury M. Personalized Digital TV: Targeting Programs to Individual Viewers (2004), Kluwer Academic Publishers
4. Balabanovic M., and Shoham Y. FAB: content-based collaborative recommender. Communications of the ACM 40 3 (1997) 66-72
5. C. Basu, H. Hirsh, W. Cohen, Recommendation as classification: using social and content-based information in recommendation, in: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98), 1998, pp. 714-720.
6. Blanco Fernández Y., Pazos Arias J.J., Gil Solla A., Ramos Cabrer M., López Nores M., and Bermejo Muñoz J. AVATAR: enhancing the personalized television by semantic inference. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) 21 2 (2007) 397-422
7. G. Boone, Concept features in RE: agent, an intelligent email agent, in: Proceedings of the 2nd International Conference on Autonomous Agents (Agents-98), 1998, pp. 141-148.
8. D. Brickley, R.V. Guha, RDF Vocabulary Description Language 1.0: RDF Schema. W3C Recommendation, 2004.
9. Cotter P., and Smyth B. A personalized TV listing service. Communications of the ACM 43 8 (2000) 107-111
10. D. Lin, An information-theoretic definition of similarity, in: Proc. of 15th International Conference on Machine Learning (ICML-98), 1998, pp. 296-304.
11. DAML: The DARPA Agent Markup Language, Disponible en http://www.daml.org, 2000.
12. DAML + OIL Revised Language Specification, Disponible en http://www.daml.org/2001/03/daml+oil-index, 2001.
13. N. Dimitrova, J. Zimmerman, A. Janevski, L. Agnihotri, N. Haas, R. Bolle, Content augmentation aspects of personalized entertainment experience, in: Proceedings of 3rd Workshop on Personalization in Future TV (TV-03), 2003, pp. 42-51.
14. Fensel D., van Harmelen F., Horrocks I., McGuinness D., and Patel-Schneider P. SOIL: an ontology infrastructure for the SemanticWeb. IEEE Intelligent Systems 16 2 (2001) 38-45
15. Ganesan P., Garcia-Molina H., and Widom J. Exploiting hierarchical domain structure to compute similarity. ACM Transactions on Information Systems 21 1 (2003) 64-93
16. P. Haase, M. Ehrig, A. Hotho, B. Schnizler, Personalized information access in a bibliographic Peer-to-Peer system, in: Proceedings of the AAAIWorkshop on Semantic Web Personalization, 2004, pp. 1-12.
17. C. Halascheck, A flexible approach for ranking complex relationships on the Semantic Web. Ph.D. thesis, Department of Computer Science, University of Georgia, 2004.
18. C. Halaschek, B. Aleman-Meza, I. Budak Arpinar, C. Ramakrishnan, A. Sheth, A flexible approach for analyzing and ranking complex relationships on the semantic web, in: Technical Report, LSDIS Lab, Computer Science Department (University of Georgia), 2004.
19. W. Hill, L. Stead, M. Rosenstein, G. Furnas, Recommending and evaluating choices in a virtual community of use, in: Proceedings of International Conference on Human Factors in Computing Systems (CHI-95), 1995, pp. 194-201.
20. G. Karvounarakis, S. Alexaki, V. Christophides, D. Plexousakis, M. Scholl, RQL: a declarative query language for RDF, in: Proceedings of the 11th World Wide Web Conference (WWW-02), 2002, pp. 115-125.
21. Konstan J., Miller B., Maltz D., Herlocker J., Gordon L., and Riedl J. GroupLens: applying collaborative filtering to usenet news. Communications of the ACM 40 3 (1997) 77-87
22. D. McGuinness, F. Harmelen, OWL Web Ontology Language Overview, W3C Recommendation, 2004.
23. S. Middleton, Capturing knowledge of user preferences with recommender systems. Ph.D. thesis, Department of Electronic and Computer Science, University of Southampton, 2003.
24. B. Miller, I. Albert, S. Lam, J. Konstan, J. Riedl, Movielens unplugged: experiences with an occasionally connected recommender system, in: Proceedings of ACM Conference on Intelligent User Interfaces (IUI-03), 2003, pp. 263-266.
25. L. Miller, A. Seaborne, A. Reggiori, Three implementations of SquishQL, a Simple RDF Query Language, in: Proceedings of the 1st International Semantic Web Conference, 2002, pp. 423-435.
26. Mobasher B., Jin X., and Zhou Y. Semantically enhanced collaborative filtering on the Web. Web Mining: Applications and Techniques (2004), IDEA Group Publishing 57-76
27. Montaner M., López B., and De la Rosa J. A taxonomy of recommender agents on the Internet. Artificial Intelligence Review 19 4 (2003) 285-330
28. S. Mukherjea, B. Bamba, BioPatentMiner: An information retrieval system for bioMedical patents, in: 30th International Conference on Very Large Data Bases (VLDB-04), 2004, pp. 1066-1077.
29. O'Sullivan D., Smyth B., Wilson D., and McDonald K. Improving the quality of the personalized EPG. User Modeling and User-Adapted Interaction 14 (2004) 5-36
30. M. Pazzani, J. Muramatsu, D. Billsus, Syskill and Webert: identifying interesting Web sites, in: Proceedings of the 13th National Conference in AI (AAAI-96), 1996, pp. 54-61.
31. M. Perry, M. Janik, C. Ramakrishnan, C. Ibañez, B. Arpinar, A. Sheth, Peer-to-Peer discovery of semantic associations, in: Proceedings of the 2nd International Workshop on Peer-to-Peer Knowledge Management (P2PKM-05), 2005, pp. 1-12.
32. A. Prata, N. Guimaraes, P. Kommers, iTV enhanced systems for generating multidevice personalized online learning environments, in: Proceedings of 4th Workshop on Personalization in Future TV (TV-04), 2004, pp. 274-280.
33. Rada R., Mili H., Bicknell E., and Blettner M. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics 19 1 (1989) 17-30
34. Resnik P. Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11 1 (1999) 95-130
35. C. Rocha, D. Schawabe, M. Poggi, A hybrid approach for searching in the Semantic Web, in: Proceedings of the 13th International World Wide Web Conference (WWW-04), 2004, pp. 74-84.
36. A. Seaborne, RDQL: A data oriented query language for RDF models, 2001. Available from: .
37. U. Shardanand, Social information filtering for music recommendation. Ph.D. thesis, Program in Media Arts and Sciences, Massachusetts Institute of Technology, 1994.
38. M. Sintek, S. Decker, TRIPLE - A query, inference and transformation language for the semantic web, in: Proceedings of the 1st International Semantic Web Conference (ISWC-02), 2002, pp. 86-96.
39. Smyth B., and Cotter P. A personalized TV listing service for the Digital TV age. Journal of Knowledge-Based Systems 13 2 (2000) 53-59
40. TiVo, Inc. TiVo: TV your way, 2002. Available from: .
41. TV-Anytime Forum, TV-Anytime specification series: S-3 on Metadata, 2003. Available from: .
42. Z. Wu, M. Palmer, Verb semantics and lexical selection, in: 32nd Annual Meeting of the Association for Computational Linguistics, New Mexico State University, Las Cruces, New Mexico, 1994, pp. 133-138.
43. C. Ziegler, L. Schmidt-Thieme, G. Lausen, Exploiting semantic product descriptions for recommender systems, in: Proceedings of the 2nd ACM SIGIR Semantic Web and Information Retrieval Workshop (SWIR-04), 2004, pp. 22-32.
44. Zimmerman J., Kurapati K., Buczak A., Schafer D., Gutta S., and Martino J. TV personalization system: design of a TV show recommender engine and interface. Personalized Digital Television: Targeting Programs to Individual Viewers (2004), Kluwer Academics Publishers pp. 27-51