A hybrid recommendation technique based on product category attributes

Expert Systems with Applications

Volumn 36, Issue 9, 2009, Pages 11480-11488

  Albadvi, Amir ^a   Shahbazi, Mohammad ^b  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b Faculty of Engineering   (Iran)

Author keywords

Customer preferences; Hybrid recommendation; Product category attribute; Product taxonomy; Recommender system

Indexed keywords

CUSTOMER PREFERENCES; HYBRID RECOMMENDATION; PRODUCT CATEGORY ATTRIBUTE; PRODUCT TAXONOMY; RECOMMENDER SYSTEM;

CUSTOMER SATISFACTION; EDUCATION; MINING; RETAIL STORES; TAXONOMIES;

SALES;

EID: 67349280287     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.03.046     Document Type: Article

References (27)

1. Adomavicius G., and Tuzhilin A. Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery 5 1-2 (2001) 33-58
2. Adomavicius G., and Tuzhilin A. Toward 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 6 (2005) 734-749
3. Boucher-Ryan P.D., and Bridge D. Collaborative recommending using formal concept analysis. Knowledge-based Systems 19 (2006) 309-315
4. Burke R. Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction 12 4 (2002) 331-370
5. Chen M.F., Tzeng G.H., and Ding C.G. Combining fuzzy AHP with MDS in identifying the preference similarity of alternatives. Journal of Applied Soft Computing 8 (2008) 110-117
6. Cheung K.-W., Kwok J.T., and Law M.H. Mining customer product ratings for personalized marketing. Decision Support Systems 35 (2003) 231-243
7. Cho Y.H., and Kim J.K. Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications 26 (2004) 233-246
8. Choi S.H., Kang S., and Jeon J.Y. Personalized recommendation system based on product specification values. Expert Systems with Applications 31 (2006) 607-616
9. Claypool, M., Gokhale, A., & Miranda, T. (1999). Combining content-based and collaborative filters in an online newspaper. In Proceedings of the ACM SIGIR workshop on recommender system.
10. Diez J., Coz J.J.D., Luaces O., and Bahamonde A. Clustering people according to their preference criteria. Expert Systems with Applications 34 (2008) 1274-1284
11. Elmore J.R., Heymann H., Johnson J., and Hewett J.E. Preference mapping: Relating acceptance of 'creaminess' to a descriptive sensory map of a semi-solid. Journal of Food Quality and Preference 10 (1999) 465-475
12. Hung L.P. A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Systems with Applications 29 (2005) 383-392
13. Karypis, G. (2001). Evaluation of item-based top-n recommendation algorithms. In Proceedings of the ACM CIKM conference (pp. 247-254).
14. Kim B.M., Li Q., Park C.S., Kim S.G., and Kim J.Y. A new approach for combining content-based and collaborative filters. Journal of Intelligent Information Systems 27 1 (2006) 79-91
15. Kleija F.T., and Musters P.D.A. Text analysis of open-ended survey responses: A complementary method to preference mapping. Journal of Food Quality and Preference 14 (2003) 43-52
16. Kuhfeld W.F. Marketing research methods in SAS. Marketing research methods in the SAS system: A collection of papers and handouts, January 1, 2005 (2005), SAS Institute Inc., Cary, NC 21-46
17. Leung C.W.-K., Chan S.C.-F., and Chung F.-L. A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems 10 3 (2006) 357-381
18. Lihua W., Lu L., Jing L., and Zongyong L. Modeling user multiple interests by an improved GCS approach. Expert Systems with Applications 29 (2005) 757-767
19. Liua D.R., and Shiha Y.Y. Integrating AHP and data mining for product recommendation based on customer lifetime value. Information and Management 42 (2005) 387-400
20. Oppewal H., and Louviere J.J. Modifying conjoint methods to model managers' reactions to business environmental trends: An application to modeling retailer reactions to sales trends. Journal of Business Research 50 (2000) 245-257
21. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the international world wide web conference (WWW10), Hong Kong (pp. 285-295).
22. Semeraro G., Lops P., and Degemmis M. WordNet-based user profiles for neighborhood formation in hybrid recommender systems. Proceedings of the 5th international conference on hybrid intelligent systems (HIS'05) (2005), IEEE 291-296
23. Senecal S., and Nantel J. The influence of online product recommendations on consumers' online choices. Journal of Retailing 80 (2004) 159-169
24. Shih Y.Y., and Liu D.R. Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Systems with Applications 35 1-2 (2008) 350-360
25. Symeonidis P., Nanopoulos A., Papadopoulos A.N., and Manolopoulos Y. Collaborative recommender systems: Combining effectiveness and efficiency. Expert Systems with Applications 34 4 (2008) 2996-3013
26. Vermeulen B., Goos P., and Vandebroek M. Models and optimal designs for conjoint choice experiments including a no-choice option. Journal of Research in Marketing 25 (2008) 94-103
27. Weng S.S., and Liu M.j. Feature-based recommendations for one-to-one marketing. Expert Systems with Applications 26 (2004) 493-508