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Issue No.05 - May (2012 vol.24)
pp: 896-911
Gediminas Adomavicius , University of Minnesota, Minneapolis
YoungOk Kwon , Sookmyung Women's University, Seoul
Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.
Recommender systems, recommendation diversity, ranking functions, performance evaluation metrics, collaborative filtering.
Gediminas Adomavicius, YoungOk Kwon, "Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 5, pp. 896-911, May 2012, doi:10.1109/TKDE.2011.15
[1] G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
[2] C. Anderson, The Long Tail. Hyperion, 2006.
[3] M. Balabanovic and Y. Shoham, "Fab: Content-Based, Collaborative Recommendation," Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.
[4] R. Bell, Y. Koren, and C. Volinsky, "The BellKor Solution to the Netflix Prize," KorBell.pdf , 2007.
[5] R.M. Bell, Y. Koren, and C. Volinsky, "The Bellkor 2008 Solution to the Netflix Prize," ProgressPrize2008BellKorSolution.pdf , 2008.
[6] J. Bennett and S. Lanning, "The Netflix Prize," Proc. KDD-Cup and Workshop at the 13th ACM SIGKDD Int'l Conf. Knowledge and Data Mining, 2007.
[7] D. Billsus and M. Pazzani, "Learning Collaborative Information Filters," Proc. Int'l Conf. Machine Learning, 1998.
[8] K. Bradley and B. Smyth, "Improving Recommendation Diversity," Proc. 12th Irish Conf. Artificial Intelligence and Cognitive Science, 2001.
[9] S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. 14th Conf. Uncertainty in Artificial Intelligence, 1998.
[10] E. Brynjolfsson, Y.J. Hu, and D. Simester, "Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales," Management Science, vol. 57, no. 8, pp. 1373-1386, 2011.
[11] E. Brynjolfsson, Y. Hu, and M.D. Smith, "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, vol. 49, no. 11, pp. 1580-1596, 2003.
[12] J. Carbonell and J. Goldstein, "The User of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries," Proc. ACM Conf. Research and Development in Information Retrieval (SIGIR), pp. 335-336, 1998.
[13] J. Delgado and N. Ishii, "Memory-Based Weighted-Majority Prediction for Recommender Systems," Proc. ACM SIGIR Workshop Recommender Systems: Algorithms and Evaluation, 1999.
[14] D. Fleder and K. Hosanagar, "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, vol. 55, no. 5, pp. 697-712, 2009.
[15] S. Funk, "Netflix Update: Try This at Home" , 2006.
[16] K.R. Gabriel and S. Zamir, "Lower Rank Approximation of Matrices by Least Squares with Any Choice of Weights," Technometrics, vol. 21, pp. 489-498, 1979.
[17] R. Garfinkel, R. Gopal, A. Tripathi, and F. Yin, "Design of a Shopbot and Recommender System for Bundle Purchases," Decision Support Systems, vol. 42, no. 3, pp. 1974-1986, 2006.
[18] A. Ghose and P. Ipeirotis, "Designing Novel Review Ranking Systems: Predicting Usefulness and Impact of Reviews," Proc. Ninth Int'l Conf. Electronic Commerce (ICEC), 2007.
[19] C. Gini, "Measurement of Inequality and Incomes," The Economic J., vol. 31, pp 124-126, 1921.
[20] D.G. Goldstein and D.C. Goldstein, "Profiting from the Long Tail," Harvard Business Rev., vol. 84, no. 6, pp. 24-28, June 2006.
[21] G.H. Golub and C. Reinsche, "Singular Value Decomposition and Least Squares Solution," Numerische Mathematik, vol. 14, pp. 403-420, 1970.
[22] K. Greene, "The $1 Million Netflix Challenge," Technology, Review.www.technologyreview.comread_article.aspx?id= 17587&ch = biztech , Oct. 2006.
[23] O.C. Herfindahl, "Concentration in the Steel Industry," Unpublished PhD dissertation, Columbia Univ., New York, 1950.
[24] J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J. Riedl, "Evaluating Collaborative Filtering Recommender Systems," ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
[25] T. Hofmann, "Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis," Proc. 26th Ann. Int'l ACM SIGIR Conf., 2003.
[26] Z. Huang, "Selectively Acquiring Ratings for Product Recommendation," Proc. Int'l Conf. Electronic Commerce, 2007.
[27] V. Klema and A. Laub, "The Singular Value Decomposition: Its Computation and Some Applications," IEEE Trans. Automatic Control, vol. AC-25, no. 2, pp. 164-176, Apr. 1980.
[28] W. Knight, "Info-Mania' Dents IQ More than Marijuana," New Scientist.comNews, http://www.newscientist.comarticle.ns?id= dn7298 , 2005.
[29] Y. Koren, "Tutorial on Recent Progress in Collaborative Filtering," Proc. ACM Conf. Recommender Systems, pp. 333-334, 2008.
[30] Y. Koren, "Collaborative Filtering with Temporal Dynamics," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 447-456, 2009.
[31] D. Lemire, S. Downes, and S. Paquet, "Diversity in Open Social Networks," technical report, University of Quebec, Montreal, 2008.
[32] S.M. McNee, J. Riedl, and J.A. Konstan, "Being Accurate Is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems," Proc. Conf. Human Factors in Computing Systems, pp. 1097-1101, 2006.
[33] D. McSherry, "Diversity-Conscious Retrieval," Proc. Sixth European Conf. Advances in Case-Based Reasoning, pp. 219-233, 2002.
[34] A. Nakamura and N. Abe, "Collaborative Filtering Using Weighted Majority Prediction Algorithms," Proc. 15th Int'l Conf. Machine Learning, 1998.
[35] S.T. Park and D.M. Pennock, "Applying Collaborative Filtering Techniques to Movie Search for Better Ranking and Browsing," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 550-559, 2007.
[36] P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Proc. Computer Supported Cooperative Work Conf., 1994.
[37] S.E. Robertson, "The Probability Ranking Principles in IR," Readings in Information Retrieval, pp. 281-286, Morgan Kaufmann Publishers, 1997.
[38] M. Sanderson, J. Tang, T. Arni, and P. Clough, "What Else Is There? Search Diversity Examined," Proc. European Conf. Information Retrieval, pp. 562-569, 2009.
[39] B.M. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Analysis of Recommender Algorithms for E-Commerce," Proc. ACM Conf. Electronic Commerce, pp. 158-167, 2000.
[40] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-Based Collaborative Filtering Recommendation Algorithms," Proc. 10th Int'l Conf. World Wide Web (WWW), 2001.
[41] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Application of Dimensionality Reduction in Recommender Systems—A Case Study," Proc. ACM WebKDD Workshop, 2000.
[42] G. Shani, D. Heckerman, and R. Brafman, "An MDP-Based Recommender System," J. Machine Learning Research, vol. 6, pp. 1265-1295, 2005.
[43] C.E. Shannon, "A Mathematical Theory of Communication," Bell System Technical J., vol. 27, pp. 379-423 and 623-656, 1948.
[44] L. Si and R. Jin, "Flexible Mixture Model for Collaborative Filtering," Proc. 20th Int'l Conf. Machine Learning, 2003.
[45] B. Smyth and K. Bradley, "Personalized Information Ordering: A Case-Study in Online Recruitment," J. Knowledge-Based Systems, vol. 16, nos. 5/6, pp. 269-275, 2003.
[46] B. Smyth and P. McClave, "Similarity vs. Diversity," Proc. Fourth Int'l Conf. Case-Based Reasoning: Case-Based Reasoning Research and Development, 2001.
[47] N. Srebro and T. Jaakkola, "Weighted Low-Rank Approximations," Proc. Int'l Conf. Machine Learning (ICML), T. Fawcett and N. Mishra, eds., pp. 720-727, 2003.
[48] X. Su and T.M. Khoshgoftaar, "Collaborative Filtering for Multi-Class Data Using Belief Nets Algorithms," Proc. Eighth IEEE Int'l Conf. Tools with Artificial Intelligence, pp. 497-504, 2006.
[49] S. ten Hagen, M. van Someren, and V. Hollink, "Exploration/Exploitation in Adaptive Recommender Systems," Proc. European Symp. Intelligent Technologies, Hybrid Systems and Their Implementation on Smart Adaptive Systems, 2003.
[50] C. Thompson, "If You Liked This, You're Sure to Love That," The New York Times, magazine 23Netflix-t.html, Nov. 2008.
[51] A. Umyarov and A. Tuzhilin, "Using External Aggregate Ratings for Improving Individual Recommendations," ACM Trans. Web, vol. 5, p. 3, 2011.
[52] M. Wu, "Collaborative Filtering via Ensembles of Matrix Factorization," Proc. KDDCup 2007, pp. 43-47, 2007.
[53] C. Zhai, W.W. Cohen, and J. Lafferty, "Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval," Proc. ACM Conf. Research and Development in Information Retrieval (SIGIR), 2003.
[54] M. Zhang and N. Hurley, "Avoiding Monotony: Improving the Diversity of Recommendation Lists," Proc. ACM Conf. Recommender Systems, pp. 123-130, 2008.
[55] S. Zhang, W. Wang, J. Ford, F. Makedon, and J. Pearlman, "Using Singular Value Decomposition Approximation for Collaborative Filtering," Proc. Seventh IEEE Int'l Conf. E-Commerce Technology (CEC '05), pp. 257-264, 2005.
[56] Z. Zheng and B. Padmanabhan, "Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution," Management Science, vol. 50, no. 5, pp. 697-712, 2006.
[57] C-N. Ziegler, S.M. McNee, J.A. Konstan, and G. Lausen, "Improving Recommendation Lists through Topic Diversification," Proc. 14th Int'l World Wide Web Conf., pp. 22-32, 2005.
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