Evaluation of Similarity Functions by using User based Collaborative Filtering approach in Recommendation Systems

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2015 by IJETT Journal
Volume-21 Number-4
Year of Publication : 2015
Authors : Shaivya Kaushik, Pradeep Tomar
DOI :  10.14445/22315381/IJETT-V21P234


Shaivya Kaushik, Pradeep Tomar"Evaluation of Similarity Functions by using User based Collaborative Filtering approach in Recommendation Systems", International Journal of Engineering Trends and Technology (IJETT), V21(4),194-200 March 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


Recommendation Systems has been comprehensively analysed and are changing from novelties used by a few E-commerce sites in the past decades.Many of the popular and largest commerce websites are widely using recommendation systems.These are popular and important part of the ecommerce ecosystem that help users to find relevant and valuable information through large product spaces.The tremendous growth of visitors and the information poses few key challenges such as producing high quality recommendation systems,performing many recommendation systems per second for millions of users and items.The paper introduce user based collaborative filtering approach and the similarity function.The algorithm will identify relationships between different users and then compute recommendation for the users.This paperpresent a most commonly used similarity functions and their computation that aims to determine which similarity function result in producing most accurate recommendation.


[1] J. Bennett and S. Lanning, “The netflix prize,” in KDD Cup and Workshop 07, 2007.
[2] Amazon.com, “Q4 2009 Financial Results,” Earnings Report Q4-2009 January 2010.
[3] L. H. Ungar and D. P. Foster.Clustering methodsfor collaborative filtering. In AAAI Workshop on Recommendation Systems, 1998.
[4] S. H. S. Chee, J. Han, and K.Wang.Rectree: An efficient collaborative filltering method.In Lecture Notes in Computer Science, pages 141{151. Springer Verlag, 2001.
[5] M. Connor and J. Herlocker.Clustering items for collaborative filtering.In Proceedings of ACM SIGIR Workshop on Recommender Systems, 2001.
[6] B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Recommender systems for large scale ecommerce: Scalable neighborhood formation using clustering. In Proceedings of the 5th International Conference on Computer and Information Technology, 2002.
[7] G. R. Xue, C. Lin, Q. Yang, W. S. Xi, H. J. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing.In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 114{121. ACM New York, NY, USA, 2005.
[8] K. Miyahara and M. J. Pazzani.Collaborative filtering with the simple bayesian classifier.In Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, pages 679{689, 2000.
[9] K. Miyahara and M. J. Pazzani.Improvement of collaborative filtering with the simple bayesian classifier 1. (11), 2002.
[10] S. Vucetic and Z. Obradovic.Collaborative filtering using a regression-basedapproach.Knowledge and Information Systems, 7:1{22, 2005.
[11] D. Lemire and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering. Society for Industrial Mathematics, 05:471{480, 2005.
[12]. Konstan, J., Miller, B., Maltz, D., Herlocker, J.Gordon, L., and Riedl, J. GroupLens: ApplyingCollaborative Filtering to Usenet News. Communications of the ACM, 40(3), 1997, 77-87.
[13] J. Breese, D. Heckerman, and C. Kadie.Empirical analysis of predictive algorithms for collaborative filtering.In Proc. of Uncertainty in Artificial Intelligence, 1998.
[14] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl.Item-based collaborative filtering recommendation algorithms.In Proc. of the international conference on World Wide Web, 2001.
[15] M. Sun, G. Lebanon, and P. Kidwell. Estimating probabilities in recommendation systems. In Proc. of the International Conference on Artificial Intelligence and Statistics,2011.
[16]. Hariri B B, Abolhassani H, and Khodaei A.: "A new Structural Similarity Measure for Ontology Alignment", in Proc. SWWS, 2006, pp.36-42.
[17]. Hongmei Wang, Sanghyuk Lee, and Jaehyung Kim.: " Quantitative Comparison of Similarity Measure and Entropy for Fuzzy Sets" ADMA `09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications, pp. 688– 695, 2009.
[18]. JouniSampo and PasiLuukka .: "Similarity Classifier with Generalized Mean; Ideal " Fuzzy Systems And Knowledge Discovery Lecture Notes in Computer Science, 2006, Volume 4223/2006, 1140-1147.
[19]. Sung-Hyuk Cha, "Comprehensive Survey on Distance/Similarity Measures between Probability Density Function", International Journal of Mathematical Models and Methods in Applied Sciences, Issues 4, Volume 1, 2007.
[20]. Randall Wilson D, Tony R. Martinez, Improved Heterogeneous Distance Functions Journal ofArtificial Intelligence Research ,Vol. 6, pp. 1-34, 1997.

Recommendation Systems, Collaborative Filtering,Similarity Functions.