Semantic inference of user's reputation and expertise to improve collaborative recommendations

Expert Systems with Applications

Volumn 39, Issue 9, 2012, Pages 8248-8258

  Martín Vicente, Manuela I ^a   Gil Solla, Alberto ^a   Ramos Cabrer, Manuel ^a   Blanco Fernández, Yolanda ^a   López Nores, Martín ^a  

^a UNIVERSITY OF VIGO   (Spain)

Author keywords

Collaborative filtering; Expertise; Personalized e commerce; Reputation; Semantic reasoning; Trust

Indexed keywords

COLLABORATIVE FILTERING; EXPERTISE; REPUTATION; SEMANTIC REASONING; TRUST;

ELECTRONIC COMMERCE; RECOMMENDER SYSTEMS; TELECOMMUNICATION NETWORKS;

SEMANTICS;

EID: 84858338497     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.01.162     Document Type: Article

References (25)

1. G. Adomavicius, and A. Tuzhilin Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions IEEE Transactions on Knowledge and Data Engineering 17 2005 734 749
2. Y. Blanco-Fernández, J. Pazos-Arias, A. Gil-Solla, M. Ramos-Cabrer, M. López-Nores, and J. Garcfa-Duque Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study The Journal of Systems and Software 81 12 2008 2371 2385
3. Y. Blanco-Fernández, J.J. Pazos-Arias, A. Gil-Solla, M. Ramos-Cabrer, M. López-Nores, and J. Garcfa-Duque Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study Journal of Systems and Software 81 12 2008 2371 2385
4. Burke, R. (1999). Integrating knowledge-based and collaborative filtering in recommender systems. In Proceedings of the workshop on AI and electronic commerce (pp. 69-72).
5. V. Carchiolo, A. Longheu, M. Malgeri, and G. Mangioni Context-based global expertise in recommendation systems Informatica 34 4 2010 409 417
6. Y.H. Cho, and J.K. Kim Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce Expert Systems with Applications 26 2 2004 233 246
7. Dima, M.; Vogiatzis, D.; Paliouras, G.; & Stamatopoulos, P. (2001). Expert based prediction of user preferences. In 5th International workshop on semantic media adaptation and personalization (pp. 112-117).
8. M. Hepp Products and services ontologies: A methodology for deriving owl ontologies from industrial categorization standards International Journal on Semantic Web & Information Systems 2 1 2006 72 99
9. M. Hepp, J. Leukel, and V. Schmitz A quantitative analysis of product categorization standards: Content, coverage and maintenance of ecl@ss, unspsc, eotd, and the rosettanet technical dictionary Knowledge and Information Systems 13 1 2007 77 114
10. A. Josang, R. Ismail, and C. Boyd A survey of trust and reputation systems for online service provision Decision Support Systems 43 2 2007 618 644
11. K. Kwon, J. Cho, and Y. Park Multidimensional credibility model for neighbor selection in collaborative recommendation Expert Systems with Applications 36 2 2009 7114 7122
12. M. López-Nores, J.J. Pazos-Arias, J. Garcfa-Duque, Y. Blanco-Fernández, M.I. Martfn-Vicente, and A. Fernández-Vilas Mispot: Dynamic product placement for digital tv through mpeg-4 processing and semantic reasoning Knowledge and Information Systems 2009 1 28
13. Martfn-Vicente, M.I.; Gil-Solla, A.; Ramos-Cabrer, M.; Blanco-Fernández, Y. and López-Nores, M. (2009). Avoiding fake neighborhoods in e-commerce collaborative recommender systems: A semantic approach. In 4th International Workshop on Semantic Media Adaptation and Personalization (pp. 9-14).
14. M.I. Martfn-Vicente, A. Gil-Solla, M. Ramos-Cabrer, Y. Blanco-Fernández, and M. López-Nores A semantic approach to avoiding fake neighborhoods in collaborative recommendation of coupons through digital tv IEEE Transactions on Consumer Electronics 56 1 2010 54 62
15. P. Massa, and P. Avesani Trust-aware collaborative filtering for recommender systems Lecture Notes in Computer Science 3290 2004 492 508
16. McDonald, D.W. (2001). Evaluating expertise recommendations. In Proceedings of the International ACM Conference on Supporting Group Work (pp. 214-223).
17. M. Montaner, B. López, and J. De la Rosa A taxonomy of recommender agents on the Internet Artificial Intelligence Review 19 4 2003 285 330
18. J. O'Donovan, and B. Smyth Mining trust values from recommendation errors International Journal on Artificial Intelligence Tools 15 6 2006 945 962
19. Sarwar, B.; Karypis, G.; Konstan, J.; Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In Proceedings of the second ACM conference on electronic commerce (pp. 158-167).
20. P. Victor, C. Cornelis, M. De Cock, and A.M. Teredesai Key figure impact in trust-enhanced recommender systems AI Communications 21 2-3 2008 127 143
21. Wang, R.Q.; Kong, F.S. (2007). Semantic-enhanced personalized recommender system. In Proceedings of the 6th International Conference on Machine Learning and Cybernetics (pp. 4069-4074).
22. Wu, P.; Liu, W. and Jin, C. (2010). A novel recommender system fusing the opinions from experts and ordinary people. In ACM international conference proceeding series (pp. 41-44).
23. Zhang, Y.; Chen, H.; Jiang, X.; Sheng, H.; Zhou, L. and Yu, T. (2008). Content-based trust mechanism for e-commerce systems. In Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, pp. 1181-1186.
24. C.-N. Ziegler, and J. Golbeck Investigating interactions of trust and interest similarity Decision Support Systems 43 2 2007 460 475
25. Ziegler, C.N.; Schmidt-Thieme, L. and Lausen, G. (2004). Exploiting semantic product descriptions for recommender systems. In 2nd ACM SIGIR semantic web and information retrieval Workshop (pp. 25-29).