Abstract

Recommender systems are smart and intelligent systems that often seem to know users more than users know themselves. Recommender system helps customers by recommending products they will probably like or purchase based on their purchasing, searching, browsing history and also the other similar customer's history. Their aim is to provide efficient personalized solution in E-commerce domain that would benefit both buyer and seller. In this paper, authors proposed a neural network based approach called Associative Memory Model (AMM) to recommend items to users and also explain Incremental AMM for dynamic dataset. Experiments are carried out to observe the performance of the proposed algorithm and compare results with the existing traditional collaborative filtering algorithm . The property of AMM is that they are able to solve the pattern completion problem. This property can be used to build an efficient recommender system for E-commerce website that can produce more accurate and quick results than the others.

References

• Ricci, F. Rokach, L. Shapira, B. , and Kantor, P. B. 2010. Recommender Systems Handbook Book. Springer.
• Sarwar, B. Karypis, G. Konstan, J. , and Riedl, J. 2000. Analysis of Recommendation Algorithms for E-Commerce. EC'00, Minneapolis, Minnesota, ACM.
• Prashant, and Dixit, M. 2013. Analysis of Neighborhood Based and Matrix Factorization Techniques for Collaborative Filtering. In Proceedings of International Conference on Information Systems and Computer Networks, 978-1-4673-5986-6, IEEE.
• Goldberg, D. Nichols, D. Oki, B. M. , and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35, 12.
• Resnick, P. Lacovou, N. Suchak, M. Bergstrom, P. , and Riedl, J. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of CSCW94, pp. 175–186.
• Breese, J. S. Heckerman, D. , and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.
• Goldberg, Roeder, K. Gupta, T. , and Perkins, C. 2000. Eigentaste: A constant time Collaborative filtering algorithm, Information Retrieval.
• Sarwar, B. Karypis, G. Konstan, J. , and Riedl, J. 2001. Item Based Collaborative Filtering Recommendation Algorithms, WWW10, Hong Kong, ACM.
• Linden, Smith, B. , and York, J. 2003. Recommendations-Item-to-Item Collaborative Filtering Greg, Amazon. com, IEEE.
• Li, Q. , and Kim, M. K. 2003. Clustering approach for hybrid recommender system. In Proceedings of the IEEE/WIC international conference on web intelligence (WI'03).
• Cheung, K. Tsui, K. , and Liu, J. 2004. Extended latent class models for collaborative filtering. IEEE Transactions on Systems Man and Cybernetics – Part A: Systems and Humans, 34(1), 143–148.
• Li, Q. Myaeng, S. H. , and Kim, M. K. 2007. A probabilistic music recommender considering user opinions and audio features. Information Processing and Management, 43, 473–487.
• Ahn, H. J. 2008. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178, 37–51.
• Kumar, S. 2004. Neural Networks: A Classroom Approach. Tata McGraw-Hill Education.
• Lee, M. Choi, P. , and Woo, Y. 2006. A Hybrid Recommender System Combining Collaborative Filtering with Neural Network. Adaptive Hypermedia and Adaptive Web-Based Systems Second International Conference, Málaga, Spain, Springer Berlin Heidelberg, pp 531-534, 978-3-540-43737-6.
• Gong, S. , and Bus, Z. 2009. An Item Based Collaborative Filtering Using BP Neural Networks Prediction. Technol. Inst. , Ningbo Hongwu Ye, 978-0-7695-3618-7, IEEE.
• Agarwal, N. 2013. A Collaborative Filtering Method Based on Associative Memory Model. In Proceedings of International Conference on Information Systems and Computer Networks, 978-1-4673-5986-6, IEEE.