Abstract

Online Social Rating Networks such as Epinions and Flixter, allow users to form handful constructive social networks, through their daily routine like recommending on the corresponding products, or similarly co-rating products. The preponderance of preceding work in Rating prognosis and Recommendation of products mainly takes into account ratings of users on products. However, in Social Rating Networks users can also construct their precise social network by reckoning each other as friends. In this paper, a perusal of different techniques for product prediction is generated.

References

• Freddy Chong Tat Chua, Hady W. Lauw, and Ee-Peng Lim. ―Generative Models for Item Adoptions Using Social Correlation‖. IEEE transaction on knowledge and data engineering, vil. 25, no,. 9, September 2013.
• Katz, Leo. (1953) A new status index derived from sociometric analysis. Psychometrika, 18(1):39-43.
• J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proc. ACM SIGIR Conf., pages 230–237, 1999.
• Liben-Nowell, David, and Kleinberg, Jon. (2007). The Link Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7):1019-1031.
• J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. on Information Systems, 22(1):5–53, 2004.
• M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. 4th ACM RecSys Conf, 2010.
• L. Kartuz. A new index derived from social analysis. Psychometrika, 18(1), 1953.
• H. Li, S. Bhowmick, and A. Sun. Affrank: Affinity-driven ranking of products in online social rating networks. Journal of the American Society for Information Science and Technology 2012.
• D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In Proc. 12th CIKM Conf., 2003.
• P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. In Proc. Federated Int. Conf. on The Move to Meaningful Internet:CoopIS, DOA, ODBASE, pages 492–508, 2004.
• B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. WWW Conf., pages 285–295, 2001.
• Rodriguez, S. (2009). Consumer Behavior Report. Economic Climate Shifts Consumers Online, 3.
• Nielson. (2010). Global Trends in Online Shopping.USA: Nielson.
• Massa, P. & Bhattacharjee, B. (2004). Using Trust in Recommender Systems: An Experimental Analysis, Trust Management: Second International Conference, 2004, Oxford, UK, March 29-April 1, 2004, Volume 2995, 2004.
• Abdul-Rahman, A. and Hailes, S., Using Recommendations for Managing Trust In Distributed Systems. In Proceedings of IEEE Malaysia International Conference on Communication, 1997.
• John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998.
• Melville P., Mooney R.L. , Nagarajan R., Content-Boosted Collaborative Filtering for Improved Recommendations, In proceedings of Eighteenth national conf. of Artificial Intelligence, 2002.
• Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan. Contentboosted collaborative filtering for improved recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), 2002.
• P. Resnick and H.R. Varian, Recommender systems, In proceedings of Communications of the ACM, 1997, 40(3).
• Mladenic, D.: Text-learning and Related Intelligent Agents: A Survey, IEEE Intelligent Systems and their applications, 1999, Vol. 14(4).
• Jeffrey D.Ullman, Jure Leskovec, Anand Rajaraman, Mining of Massive Datasets, Cambridge University Press.
• Byeong Man Kim & Qing Li & Chang Seok Park & Si Gwan Kim & Ju Yeon Kim, A new approach for combining content-based and collaborative filters, J Intell Inf Syst 2006, Vol 27.
• Nitin Agarwal, Ehtesham Haque, Huan Liu, and Lance Parsons, Research Paper Recommender Systems: A Subspace Clustering Approach, In International Conference on Web-Age Information Management.
• C. Basu, H. Hirsh, and W. Cohen. Recommendation as Classification: Using social and content-based information in recommendation. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98).
• Mahmood, T., Ricci, F.: Improving recommender systems with adaptive conversational strategies. In: C. Cattuto, G. Ruffo, F. Menczer (eds.) Hypertext, pp. 73–82. ACM (2009).
• Burke, R.: Hybrid web recommender systems. In the Adaptive Web, 2007 Vol 4321.
• Zhou, Daniel and Resnick, Paul, Assessment of conversation co-mentions as a resource for software module recommendation, In Proceedings of RecSys '09. ACM, New York, NY.
• Luhn, H. P, A statistical approach to mechanized encoding and searching of literary information, In IBM Journal of Research and Development, 1957, Vol 1(4).
• Dennis, S. F, In M. E. Stevens, V. E. Giuliano, & L. B. Heilprin (Eds.), Statistical association methods for mechanized documentation, Symposium Proceedings (Miscellaneous publication 269). Washington, DC: National Bureau of Standards.
• Salton, G., & Buckley, C., Weighting approaches in automatic text retrieval, In Information Processing and Management, 1988, Vol. 24(5).
• Sparck-Jones, K., A statistical interpretation of term specificity and its application in retrieval, In Journal of Documentation, 1972, Vol. 28(1).
• Robertson, S. E., & Sparck-Jones, K, Relevance weighting of search terms, In Journal of the American Society of Information Science, 1976 Vol. 27.
• Slonim, N., & Tishby, N, Document clustering using word clusters via the information bottleneck method, In Proceedings of the 23rd international conference on research and development in information retrieval, 2000.