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Issue No.08 - August (2009 vol.21)
pp: 1133-1146
Milan Vojnović; , Microsoft Research Ltd., Cambridge
James Cruise , Bristol University, Bristol
Dinan Gunawardena , Microsoft Research Ltd., Cambridge
Peter Marbach , University of Toronto, Toronto
We consider the problem of ranking the popularity of items and suggesting popular items based on user feedback. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either from this suggestion set or from the set of all possible items. The goal is to quickly learn the true popularity ranking of items (unbiased by the made suggestions), and suggest true popular items. The difficulty is that making suggestions to users can reinforce popularity of some items and distort the resulting item ranking. The described problem of ranking and suggesting items arises in diverse applications including search query suggestions and tag suggestions for social tagging systems. We propose and study several algorithms for ranking and suggesting popular items, provide analytical results on their performance, and present numerical results obtained using the inferred popularity of tags from a month-long crawl of a popular social bookmarking service. Our results suggest that lightweight, randomized update rules that require no special configuration parameters provide good performance.
Popularity ranking, recommendation, suggestion, implicit user feedback, search query, social tagging.
Milan Vojnović;, James Cruise, Dinan Gunawardena, Peter Marbach, "Ranking and Suggesting Popular Items", IEEE Transactions on Knowledge & Data Engineering, vol.21, no. 8, pp. 1133-1146, August 2009, doi:10.1109/TKDE.2009.34
[1] V. Anantharam, P. Varaiya, and J. Walrand, “Asymptotically Efficient Allocation Rules for the Multiarmed Bandit Problem with Multiple Plays—Part i: i.i.d. Rewards,” IEEE Trans. Automatic Control, vol. 32, no. 11, pp.968-976, Nov. 1987.
[2] J.R. Anderson, “The Adaptive Nature of Human Categorization,” Psychological Rev., vol. 98, no. 3, pp. 409-429, 1991.
[3] A.L. Barabási and R. Albert, “Emergence of Scaling in Random Networks,” Science, vol. 286, pp.509-512, 1999.
[4] S. Brams and P. Fishburn, Approval Voting. Birkhauser, 1983.
[5] S. Chakrabarti, A. Frieze, and J. Vera, “The Influence of Search Engines on Preferential Attachment,” Proc. Symp. Discrete Algorithms (SODA), 2005.
[6] J. Cho, S. Roy, and R.E. Adams, “Page Quality: In Search of an Unbiased Web Ranking,” Proc. ACM SIGMOD '05, 2005.
[7] S. Golder and B.A. Huberman, “The Structure of Collaborative Tagging Systems,” J. Information Science, vol. 32, no. 2, pp.198-208, 2006.
[8] / / , 2009.
[9] J. Kleinberg and M. Sandler, “Using Mixture Models for Collaborative Filtering,” Proc. 36th Ann. ACM Symp. Theory of Computing (STOC), 2004.
[10] R. Kumar, P. Rajagopalan, and A. Tomkins, “Recommendation Systems: A Probabilistic Analysis,” Proc. 39th Ann. Symp. Foundations of Computer Science (FOCS), 1998.
[11] T.L. Lai and H. Robbins, “Asymptotically Efficient Adaptive Allocation Rules,” Advances in Applied Math., vol. 6, pp. 4-25, 1985.
[12] T.M. Liggett, Interacting Particle Systems, second ed. Springer, 2006.
[13] R.D. Luce, Individual Choice Behavior: A Theoretical Analysis. Dover, 1959.
[14] R.M. Nosofsky, “Choice, Similarity, and the Context Theory of Classification,” J. Experimental Psychology, vol. 10, no. 1, pp.104-114, 1984.
[15] S. Pandey, S. Roy, C. Olston, J. Cho, and S. Chakrabarti, “Shuffling Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results,” Proc. 31st Int'l Conf. Very Large Data Bases (VLDB), 2005.
[16] R.M. Phatarfod, “On the Matrix Occurring in a Linear Search Problem,” J. Applied Probability, vol. 18, pp.336-346, 1991.
[17] G. Salton and M.J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill Education, 1983.
[18] S. Sen, S.K. Lam, A.-M. Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F.M. Harper, and J. Riedl, “Tagging, Communities, Vocabulary, Evolution,” Proc. 2006 20th Anniversary Conf. Computer Supported CooperativeWork (CSCW), 2006.
[19] H.A. Simon, “Bandwagon and Underdog Effects and the Possibility of Election Predictions,” Public Opinion Quarterly, vol. 18, pp.245-253, 1954.
[20] F. Suchanek, M. Vojnović, and D. Gunawardena, “Social Tagging: Meaning and Suggestions,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), Oct. 2008.
[21] C.J. van Rijsbergen, Information Retrieval, second ed. Butterworths,, 1979.
[22] M. Vojnović, J. Cruise, D. Gunawardena, and P. Marbach, “Ranking and Suggesting Tags in Collaborative Tagging Applications,” Technical Report MSR-TR-2007-06, Microsoft Research, Feb. 2007.
[23] Z. Xu, Y. Fu, J. Mao, and D. Su, “Towards the Semantic Web: Collaborative Tag Suggestions,” Proc. Workshop Collaborative Web Tagging Workshop at the WWW 2006, May 2006.
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