A MULTI-CRITERIA RECOMMENDATION SYSTEM BASED ON HYBRID PROFILE
Keywords:
Collaborative Filtering, Content-based, Hybrid methodAbstract
This paper proposes a “Multi-criteria Recommender” system based on explicit gathering of users’ preferences. Normally within data gathering process, a user is asked to rate different aspects of an item founded on a sliding scale explicitly. However, individual’s preferences on each aspect of item may conflict with other preferences. To overcome such conflicts and limitations, we proposed a “Hybrid Method” derived from gathering of previous data profiles. The method creates a hybrid profile through combination of user-item data and item-related data. Having the calculated aggregated data, we can allocate global criteria weights to preferences of users. Despite of other methods, we did not use overall rating method at all. Instead, we tried to localize the obtained weights for each individual user. Consequently, the system applies the allocated weights in a way to recommend appropriate calculated items for each individual user.
References
Adomavicius, Gediminas, and Youngok Kwon. 2007. New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intelligent Systems 22, no. 3: 48-55.
Adomavicius and Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the. Knowledge and Data Engineering IEEE Transactions on 17, no. 6: 734--749.
Balabanovic, Marko, and Yoav Shoham. 1997. Fab : content-based , collaborative recommendation .( Special Section : Recommender Systems ). Commu nications of the ACM 66, March: 1-7.
Billsus, D., C. A. Brunk, C. Evans, B. Gladish, and M. Pazzani. Adaptive interfaces for ubiquitous web access. Commnications of the ACM, 45 no.5:34-38, 2002.
Bollen, Dirk, Bart P Knijnenburg, Martijn C Willemsen, and Mark Graus. 2010. Understanding choice overload in recommender systems. ACM International Conference on Recommender Systems RecSys, 63-70.
Burke, Robin. 2000. Knowledge-based recommender systems. Encyclopedia of Library and Information Systems 69, no. Supplement 32: 175–186.
Burke, Robin. 2002. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User Adapted Interaction 12, no. 4: 331-370.
Burke, Robin. 2007. Hybrid Recommender Systems: The Adaptive Web. Springer Berlin Heidelberg: 377-408.
Deshpande, Mukund, and George Karypis. 2004. Item-Based Top-N Recommendation Algorithms. ACM Transactions on Information Systems 22, no. 1: 143-177.
Herlocker, J L, J A Konstan, A Borchers, and J Riedl. 1999. An algorithmic framework for performing collaborative filtering. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 54: 230-237.
Kellar, M, C Watters, J Duffy, and M Shepherd. 2004. Effect of Task on Time Spent Reading as an Implicit Measure of Interest. Proc ASIS AM 04.
Konstan, J A, B N Miller, David Maltz, J L Herlocker, L R Gordon, and John Riedl. 1997. GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40, no. 3: 77-87.
Lee, T, and Y Park. 2008. A time-based approach to effective recommender systems using implicit feedback. Expert Systems with Applications 34, no. 4: 3055-3062.
Lee, Wei-Po, Chih-Hung Liu, and Cheng-Che Lu. 2002. Intelligent agent-based systems for personalized recommendations in Internet commerce. Expert Systems with Applications 22, no. 4: 275-284.
Linden, G., B. Smith, and J. York. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, Jan.-Feb, 2003.
Manouselis, Nikos, and Constantina Costopoulou. 2007. Analysis and Classification of Multi-Criteria Recommender Systems. World Wide Web Internet and Web Information Systems 10, no. 4: 415-441.
Manouselis, Nikos. 2007. Experimental Analysis of Design Choices in Multiattribute Utility Collaborative Filtering. International Journal of Pattern Recognition and Artificial Intelligence 21, no. 2: 311.
Middleton, Stuart E, N R Shadbolt, and D C De Roure. 2004. Ontological User Profiling in Recommender Systems. ACM Transactions on Information Systems 22, no.1: 54-88.
Miller, Bradley N, Istvan Albert, Shyong K Lam, Joseph A Konstan, and John Riedl. 2003. MovieLens unplugged: experiences with an occasionally connected recommender system. Proceedings of the 8th international conference on Intelligent user interfaces: 263-266.
Marlin, B. Modeling User Rating Profiles for Collaborative Filtering. Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS’03), 2003.
MovieLens, Retrieved January 29, 2012 from http://www.grouplens.org/node/73/.
Oard, D., & Kim, J. (1998). Implicit Feedback for Recommender Systems. Proceedings of the AAAI Workshop on Recommender Systems: 81-83.
O’Donovan, John, and Barry Smyth. 2005. Trust in recommender systems. Proceedings of the 10th international conference on Intelligent user interfaces IUI 05 15, June: 167.
Palanivel K. and Sivakumar R. 2010. A Study On Implicit Feedback in Multicriteria E-Commerce Recommender System. Journal of Electronic Commerce Research 11, no. 2: 140-156.
Pazzani, Michael J. 1999. A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13, no. 5: 393-408.
Pittaya Poompiang, Wichian Premchaiswadi. 2010. User and Item Pattern Matching in Multi-criteria Recommender Systems. 11th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, SNPD 2010, London, England, UK, June 9-11, 2010: 20-25.
Recker, Mimi M, and Andrew Walker. 2003. Supporting “word-of-mouth” social networks through collaborative information filtering. Journal Of Interactive Learning Research 14, no. 1.
Ricci, F, and H Werthner. 2002. Case base querying for travel planning recommendation. Tourism 3, no. 4: 215-226.
Rosset, S., E. Neumann, U. Eick, N. Vatnik, and Y. Idan. Customer Lifetime Value Modeling and Its Use for Customer Retention Planning. Proceeding of the 8th ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining (KDD-2002), July 2002.
Schafer, J Ben, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative Filtering Recommender Systems. International Journal of Electronic Business 2, no. 1: 77.
Schafer, J Ben, Joseph A Konstan, and John Riedl. 2001. E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery 5, no. 1: 115-153.
Schafer, J Ben, Joseph Konstan, and John Riedi. 1999. Recommender systems in e-commerce. Proceedings of the 1st ACM conference on Electronic commerce EC 99: 158-166.
Takács, Gábor, István Pilászy, Bottyán Németh, and Domonkos Tikk. 2008. Matrix factorization and neighbor based algorithms for the netflix prize problem. Proceedings of the 2008 ACM conference on Recommender systems RecSys 08: 267.
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