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A Hybrid Approach to Solve Cold Start Problem in Recommender Systems using Association Rules and Clustering Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 4 |
| Year of Publication: 2013 |
| Authors: Hridya Sobhanam, A. K. Mariappan |
10.5120/12873-9697
|
Hridya Sobhanam, A. K. Mariappan . A Hybrid Approach to Solve Cold Start Problem in Recommender Systems using Association Rules and Clustering Technique. International Journal of Computer Applications. 74, 4 ( July 2013), 17-23. DOI=10.5120/12873-9697
Abstract
Number of people who use internet and websites for various purposes is increasing at an astonishing rate. More and more people rely on online sites for purchasing songs, apparels, books, rented movies etc. The competition between the online sites forced the web site owners to provide personalized services to their customers. So the recommender systems came into existence. Recommender systems are active information filtering systems that attempt to present to the user, information items in which the user is interested in. The websites implement recommender system feature using collaborative filtering, content based or hybrid approaches. The recommender systems also suffer from issues like cold start, sparsity and over specialization. Cold start problem is that the recommenders cannot draw inferences for users or items for which it does not have sufficient information. This paper attempts to propose a solution to the cold start problem by combining association rules and clustering technique. Comparison is done between the performance of the recommender system when association rule technique is used and the performance when association rule and clustering is combined. The experiments with the implemented system proved that accuracy can be improved when association rules and clustering is combined. An accuracy improvement of 36% was achieved by using the combination technique over the association rule technique.
References
- Adomavicius, G. , Tuzhilin, A. 2005. Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactionson Knowledge and Data Engineering 17, 734–749.
- Schein, A. I. , Popescul, A. , Ungar, L. H. , Pennock, M. 2002. Methods and Metrics forCold-Start Recommendations. In: 25th Annual International ACM SIGIR Conferenceon Research and Development in Information Retrieval (SIGIR'02). Pp. 253–260. Tampere, Finland.
- Middleton, S. E. , Alani, H. , Shadbolt, N. R. , Roure, and D. C. D. 2002. Exploiting Synergybetween Ontologies and Recommender Systems. In: The Semantic Web Workshop,World Wide Web Conference (WWW'02). pp. 41–50. Hawaii, USA.
- Ziegler, C. N. , Lausen, G. , Schmidt-Thieme, L. 2004. Taxonomy-driven Computationof Product Recommendations. In: International Conference on Information andKnowledge Management (CIKM'04). pp. 406–415. Washington D. C. , USA.
- Leung, C. W. , Chan, S. C. , Chung, F. 2007. Applying Cross-level Association Rule Miningto Cold-Start Recommendations. In: IEEE/WIC/ACM International Conferenceon Web Intelligence and Intelligent Agent Technology - Workshops. pp. 133–136. Silicon Valley, California, USA. M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989.
- Pasquier, N. , Taouil, R. , Bastide, Y. , Stumme, G. 2005. Generating a Condensed Representationfor Association Rules. Journal of Intelligent Information Systems 24,29–60.
- Shaw, G. , Xu, Y. , Geva, S. 2008. Eliminating Association Rules in Multi-level Datasets. In: In 4th International Conference on Data Mining (DMIN'08). pp. 313–319. LasVegas, USA.
- Shaw, G. , Xu, Y. , Geva, S. 2008. Extracting Non-Redundant Approximate Rules fromMulti-Level Datasets. In: In 20th IEEE International Conference on Tools withArtificial Intelligence (ICTAI'08). pp. 333–340. Dayton, Ohio, USA.
- J. Herlocker, J. Konstan, B. A. , and J. Riedl. 1999. An algorithmic framework for performing collaborative filtering. In Proc. ACM-SIGIR Conf. , pages 230–237.
- Q. Li and B. Kim. 2003. An approach for combining content-based and collaborative filters. In Proc. of IRAL2003.
- J. B. Schafer, J. Konstan, and J. Riedl. 1999. Recommender systemsin e-commerce. In Proc. 1st ACM. Conf. on ElectronicCommerce (EC'99).
- Al Mamunur Rashid, George Karypis, and John Riedl. Learning Preferences of New Users in Recommender Systems: An Information Theoretic Approach(Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN-55455)
- Yue Xu, Gavin Shaw, Yue feng Li. 2009. Concise Representations for Association Rules in Multi-level datasets(ISSN: 1004-3756 (Paper) 1861- 9576 (Online) DOI: 10. 1007/s11518-009-5098-x CN11-2983/N, © Systems Engineering Society of China & Springer-Verlag 2009)
- Sunita B Aher, Lobo L. M. R. J. 2012. Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning(International Journal Of Computer Applications (0975 – 8887) Volume 41– No. 6.
- Hui Lia, Xinyue Liub. 2012. A Personalized Recommendation System Combining User Clusteringand Association Rules with Multiple Minimum Supports in 2nd International Conference on Future Computers in Education Lecture Notes in Information Technology, Vols. 23-24.
- Jonathan L. Herlocker, Joseph A Konstan, Loren G Terveen and John T Riedl. 2004. Evaluating Collaborative Filtering Recommender Systems in ACM 1046-8188/04/0100-0005 $5. 00 ACM Transactions on Information Systems, Vol. 22, No. 1, Pages 5–53.
- Amit Singhal. 2001. Modern Information Retrieval: A Brief Overview(Copyright 2001 IEEE. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering)
- Badrul M. Sarwar, George Karypis, Joseph Konstan,John Riedl. Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering. (GroupLens Research Group /Army HPC Research Center)
- Qing Li, Byeong Man Kim. 2003. Clustering Approach for Hybrid Recommender System, Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI'03)© 2003 IEEE
- Shaw, Gavin, Xu, Yue, & Geva, Shlomo. 2010. Using Association Rules to Solvethe Cold-Start Problem in Recommender Systems, QUT Digital Repository:http://eprints. qut. edu. au/40176,© Copyright 2010 Springer
- Harald Steck. 2010. Training and Testing of Recommender Systems on Data missing not at random, KDD '10 -July 2010, Copyright 2010 ACM 978-1-4503-0055-1/10/07.
- Sobhanam Hridya, Mariappan,A. K. 2013. Addressing cold start problem in recommender systemsusing association rules and clustering technique. International Conference on Computer Communication and Informatics (ICCCI- 2013), Coimbatore, India. Print ISBN: 978-1-4673-2906-4.
- J. Han and M. Kamber. 2000. Data mining: Concepts and Techniques. Morgan-Kaufman, New York.
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