Accuracy improvements for multi-criteria recommender systems

Proceedings of the ACM Conference on Electronic Commerce

Volumn , Issue , 2012, Pages 674-689

  Jannach, Dietmar ^a   Karakaya, Zeynep ^a   Gedikli, Fatih ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

machine learning; multi criteria ratings; recommender systems

Indexed keywords

ACCURACY IMPROVEMENT; AUTOMATIC ADJUSTMENT; COLLABORATIVE FILTERING; COMBINATION WEIGHTS; DATA SETS; E-COMMERCE SITES; EXPERIMENTAL ANALYSIS; MATRIX FACTORIZATIONS; MULTI-CRITERIA; PREDICTIVE ACCURACY; PRODUCT CATALOGS; PRODUCT RECOMMENDATION; REGRESSION MODEL; SUPPORT VECTOR REGRESSION (SVR); USER COMMUNITIES;

CUSTOMER SATISFACTION; LEARNING SYSTEMS; RATING; RECOMMENDER SYSTEMS; REGRESSION ANALYSIS; SALES; WEBSITES;

ELECTRONIC COMMERCE;

EID: 84863534047     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2229012.2229065     Document Type: Conference Paper

References (32)

1. ADOMAVICIUS, G. AND KWON, Y. 2007. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems 22, 48-55.
2. ADOMAVICIUS, G., MANOUSELIS, N., AND KWON, Y. 2011. Multi-criteria recommender systems. In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer US, 769-803.
3. BURKE, R. 2002. Interactive critiquing forcatalog navigation in e-commerce. Artificial Intelligence Review 18, 3-4, 245-267.
4. DIAS, M. B., LOCHER, D., LI, M., EL-DEREDY, W., AND LISBOA, P. J. 2008. The value of personalised recommender systems to e-business: a case study. In Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys'08). Lausanne, Switzerland, 291-294.
5. DRUCKER, H., CHRIS, KAUFMAN, B. L., SMOLA, A., AND VAPNIK, V. 1997. Support vector regression machines. In Advances in Neural Information Processing Systems 9. Vol. 9. 155-161.
6. FLEDER, D. M. AND HOSANAGAR, K. 2007. Recommender systems and their impact on sales diversity. In Proceedings of the 8th ACM Conference on Electronic Commerce (EC'07). San Diego, California, USA, 192-199.
7. GEDIKLI, F., BAGDAT, F., GE, M., AND JANNACH, D. 2011. RF-REC: Fast and accurate computation of recommendations based on rating frequencies. In Proceedings of the IEEE Conference on Commerce and Enterprise Computing (CEC 2011). Luxembourg, Luxembourg, 50-57.
8. GEDIKLI, F. AND JANNACH, D. 2010. Neighborhood-restricted mining and weighted application of association rules for recommenders. In Proceedings of the 11th International Conference on Web Information Systems Engineering (WISE 2010). Hong Kong, China, 157-165.
9. GEDIKLI, F. AND JANNACH, D. 2012. Improving recommendation accuracy based on item-specific tag preferences. ACM Transactions on Intelligent Systems and Technology, forthcoming.
10. GENTLEMAN, W. M. 1974. Algorithm as 75: Basic procedures for large, sparse or weighted linear least problems. Journal of the Royal Statistical Society. Series C (Applied Statistics) 23, 3, 448-454.
11. HOFMANN, T. 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22, 89-115.
12. JANNACH, D. 2006. Finding preferred query relaxations in content-based recommenders. In 3rd International IEEE Conference on Intelligent Systems. London, 355-360.
13. JANNACH, D. AND HEGELICH, K. 2009. A case study on the effectiveness of recommendations in the mobile internet. In Proceedings of the 2009 ACM Conference on Recommender Systems (RecSys'09). New York, NY, USA, 41-50.
14. JANNACH, D. AND KREUTLER, G. 2005. Personalized user preference elicitation for e-services. In IEEE International Conference on e-Technology, e-Commerce,and e-Services (EEE 2005). 604-611.
15. JANNACH, D., ZANKER, M., FELFERNIG, A., AND FRIEDRICH, G. 2010. Recommender Systems - An Introduction. Cambridge University Press.
16. JANNACH, D., ZANKER, M., AND FUCHS, M. 2009. Constraint-based recommendation in tourism: A multiperspective case study. Journal of Information Technology and Tourism 11, 2, 139-155.
17. KOREN, Y. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data 4, 1-24.
18. LEMIRE, D. AND MACLACHLAN, A. 2005. Slope one predictors for online rating-based collaborative filtering. In Proceedings of the 5th SIAM International Conference on Data Mining (SDM'05). Newport Beach,CA, 471-480.
19. LI, Q., WANG, C., AND GENG, G. 2008. Improving personalized services in mobile commerce by a novel multicriteria rating approach. In Proceedings of the 17th International Conference on World Wide Web. Beijing, China, 1235-1236.
20. LINDEN, G., SMITH, B., AND YORK, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing, IEEE 7, 1, 76-80.
21. LIU, H. AND SETIONO, R. 1995. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of the Seventh International Conference on Tools with Artificial Intelligence. Vancouver, Canada, 388-391.
22. LIU, L., MEHANDJIEV, N., AND XU, D.-L. 2011. Multi-criteria service recommendation based on user criteria preferences. In Proceedings of the fifth ACM Conference on Recommender Systems (RecSys 2011). Chicago, IL, USA, 77-84.
23. MANOUSELIS, N. AND COSTOPOULOU, C. 2007. Analysis and classification of multi-criteria recommender systems. World Wide Web 10, 415-441.
24. MIERSWA, I. 2009. Non-convex and multi-objective optimization in data mining, Doctoral thesis, Department of Computer Science, TU Dortmund, Germany.
25. NAKAGAWA, M. AND MOBASHER, B. 2003. A hybrid web personalization model based on site connectivity. In Proceedings of the Workshop on Web Mining and Web Usage Analysis (WebKDD'03). Washington, DC, USA, 59-70.
26. SAHOO, N., KRISHNAN, R., DUNCAN, G., AND CALLAN, J. P. 2006. Collaborative filtering with multicomponent rating for recommender systems. In Proceedings of the Sixteenth Annual Workshop on Information Technologies and Systems (WITS'06).
27. SAHOO, N., KRISHNAN, R., DUNCAN, G., AND CALLAN, J. P. 2011. The Halo Effect in multi-component ratings and its implications for recommender systems: The case of Yahoo! Movies. Information Systems Research.
28. SEN, S., VIG, J., AND RIEDL, J. 2009. Tagommenders: Connecting users to items through tags. In Proceedings of the 18th International World Wide Web Conference (WWW'09). Madrid, Spain, 671-680.
29. SENECAL, S. AND NANTEL, J. 2004. The influence of online product recommendations on consumers' online choices. Journal of Retailing 80, 2, 159-169.
30. SI, L. AND JIN, R. 2003. Flexible mixture model for collaborative filtering. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003). Washington, DC, 704-711.
31. ZANKER, M., BRICMAN, M., GORDEA, S., JANNACH, D., AND JESSENITSCHNIG, M. 2006. Persuasive online-selling in quality & taste domains. In Proceedings of the 7th International Conference on Electronic Commerce and Web Technologies (EC-Web'06). Springer, Krakow, Poland, 51-60.
32. ZHANG, Y., ZHUANG, Y., WU, J., AND ZHANG, L. 2009. Applying probabilistic latent semantic analysis to multi-criteria recommender system. AI Communications 22, 97-107.