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Issue No.02 - February (2011 vol.23)
pp: 190-203
Ilaria Bartolini , Università di Bologna, Bologna
Zhenjie Zhang , Advanced Digital Sciences Center, Illinois at Singapore Pte., Singapore
Dimitris Papadias , Hong Kong University of Science and Technology, Hong Kong
Collaborative filtering (CF) systems exploit previous ratings and similarity in user behavior to recommend the top-k objects/records which are potentially most interesting to the user assuming a single score per object. However, in various applications, a record (e.g., hotel) maybe rated on several attributes (value, service, etc.), in which case simply returning the ones with the highest overall scores fails to capture the individual attribute characteristics and to accommodate different selection criteria. In order to enhance the flexibility of CF, we propose Collaborative Filtering Skyline (CFS), a general framework that combines the advantages of CF with those of the skyline operator. CFS generates a personalized skyline for each user based on scores of other users with similar behavior. The personalized skyline includes objects that are good on certain aspects, and eliminates the ones that are not interesting on any attribute combination. Although the integration of skylines and CF has several attractive properties, it also involves rather expensive computations. We face this challenge through a comprehensive set of algorithms and optimizations that reduce the cost of generating personalized skylines. In addition to exact skyline processing, we develop an approximate method that provides error guarantees. Finally, we propose the top-k personalized skyline, where the user specifies the required output cardinality.
Skyline, collaborative filtering.
Ilaria Bartolini, Zhenjie Zhang, Dimitris Papadias, "Collaborative Filtering with Personalized Skylines", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 2, pp. 190-203, February 2011, doi:10.1109/TKDE.2010.86
[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] M. Balabanovic and Y. Shoham, "Fab: Content-Based, Collaborative Recommendation," Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.
[3] I. Bartolini, P. Ciaccia, and M. Patella, "Efficient Sort-Based Skyline Evaluation," ACM Trans. Database Systems, vol. 33, no. 4, pp. 1-49, 2008.
[4] C. Basu, H. Hirsh, and W.W. Cohen, "Recommendation as Classification: Using Social and Content-Based Information in Recommendation," Proc. Conf. Am. Assoc. Artificial Intelligence (AAAI), 1998.
[5] S. Börzsönyi, D. Kossmann, and K. Stocker, "The Skyline Operator," Proc. 17th Int'l Conf. Data Eng. (ICDE), 2001.
[6] J.S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. 14th Conf. Uncertainty in Artificial Intelligence (UAI), 1998.
[7] R. Burke, "Hybrid Recommender Systems: Survey and Experiments," User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002.
[8] C.Y. Chan, P.-K. Eng, and K.-L. Tan, "Stratified Computation of Skylines with Partially-Ordered Domains," Proc. ACM SIGMOD, 2005.
[9] C.Y. Chan, H. Jagadish, K.-L. Tan, A. Tung, and Z. Zhang, "Finding $k$ -Dominant Skylines in High Dimensional Space," Proc. ACM SIGMOD, 2006.
[10] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, "Skyline with Presorting," Proc. 19th Int'l Conf. Data Eng. (ICDE), 2003.
[11] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, "Combining Content-Based and Collaborative Filters in an Online Newspaper," Proc. ACM SIGIR Workshop Recommender Systems, 1999.
[12] D. Cosley, S. Lawrence, and D.M. Pennock, "REFEREE: An Open Framework for Practical Testing of Recommender Systems Using ResearchIndex," Proc. 28th Int'l Conf. Very Large Data Bases (VLDB), 2002.
[13] E. Dellis and B. Seeger, "Efficient Computation of Reverse Skyline Queries," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), 2007.
[14] P. Godfrey, R. Shipley, and J. Gryz, "Maximal Vector Computation in Large Data Sets," Proc. 31st Int'l Conf. Very Large Data Bases (VLDB), 2005.
[15] D. Goldberg, D. Nichols, B.M. Oki, and D. Terry, "Using Collaborative Filtering to Weave an Information Tapestry," Comm. ACM, vol. 35, no. 12, pp. 61-70, 1992.
[16] N. Good, J.B. Schafer, J.A. Konstant, A. Borchers, B. Sarwar, J. Herlocker, and J. Riedl, "Combining Collaborative Filtering with Personal Agents for Better Recommendations," Proc. Conf. Am. Assoc. Artificial Intelligence (AAAI), 1999.
[17] J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl, "Evaluating Collaborative Filtering Recommender Systems," ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
[18] Z. Huang, C.S. Jensen, H. Lu, and B.C. Ooi, "Skyline Queries against Mobile Lightweight Devices in MANETs," Proc. 22nd Int'l Conf. Data Eng. (ICDE), 2006.
[19] M. Khalefa, M. Mokbel, and J. Levandoski, "Skyline Query Processing for Incomplete Data," Proc. 24th Int'l Conf. Data Eng. (ICDE), 2008.
[20] D. Kossmann, F. Ramsak, and S. Rost, "Shooting Stars in the Sky: An Online Algorithm for Skyline Queries," Proc. 28th Int'l Conf. Very Large Data Bases (VLDB), 2002.
[21] H. Kung, F. Luccio, and F. Preparata, "On Finding the Maxima of a Set of Vectors," J. ACM, vol. 22, no. 4, pp. 469-476, 1975.
[22] K. Lee, B. Zheng, H. Li, and W. Lee, "Approaching the Skyline in Z Order," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), 2007.
[23] W.S. Lee, "Collaborative Learning for Recommender Systems," Proc. 18th Int'l Conf. Machine Learning (ICML), 2001.
[24] X. Lian and L. Chen, "Monochromatic and Bichromatic Reverse Skyline Search over Uncertain Databases," Proc. ACM SIGMOD, 2008.
[25] D. McLain, "Drawing Contours from Arbitrary Data Points," Computer J., vol. 17, no. 4, pp. 318-324, 1974.
[26] M. Mitzenmacher and E. Upfal, Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge Press, 2005.
[27] M. Morse, J. Patel, and W. Grosky, "Efficient Continuous Skyline Computation," Proc. 22nd Int'l Conf. Data Eng. (ICDE), 2006.
[28] M. Morse, J. Patel, and H. Jagadish, "Efficient Skyline Computation over Low-Cardinality Domains," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), 2007.
[29] D. Papadias, Y. Tao, G. Fu, and B. Seeger, "Progressive Skyline Computation in Database Systems," ACM Trans. Database Systems, vol. 30, no. 1, pp. 41-82, 2005.
[30] M. Pazzani and D. Billsus, "Learning and Revising User Profiles: The Identification of Interesting Web Sites," Machine Learning, vol. 27, pp. 313-331, 1997.
[31] J. Pei, B. Jiang, X. Lin, and Y. Yuan, "Probabilistic Skyline on Uncertain Data," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), 2007.
[32] D.M. Pennock, E. Horvitz, and C.L. Giles, "Social Choice Theory and Recommender Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering," Proc. Conf. Am. Assoc. Artificial Intelligence (AAAI), 2000.
[33] D.M. Pennock, E. Horvitz, S. Lawrence, and C.L. Giles, "Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach," Proc. Conf. Am. Assoc. Artificial Intelligence (AAAI), 2000.
[34] P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Proc. ACM Conf. Computer Supported Cooperative Work (CSCW), 1994.
[35] P. Resnick and H.R. Varian, "Recommender Systems," Comm. ACM, vol. 40, no. 3, pp. 56-58, 1997.
[36] E. Rich, "User Modeling via Stereotypes," Cognitive Science, vol. 3, no. 4, pp. 329-354, 1979.
[37] C. Shahabi, F. Banaei-Kashani, Y. Chen, and D. Yoda McLeod, "An Accurate and Scalable Web-Based Recommendation System," Proc. Ninth Int'l Conf. Cooperative Information Systems (COOPIS), 2001.
[38] U. Shardanand and P. Maes, "Social Information Filtering: Algorithms for Automating 'Word of Mouth'," Proc. SIGCHI Conf. Human Factors in Computing Systems (CHI), 1995.
[39] M. Sharifzadeh and C. Shahabi, "The Spatial Skyline Queries," Proc. 32nd Int'l Conf. Very Large Data Bases (VLDB), 2006.
[40] R. Steuer, Multiple Criteria Optimization. Wiley, 1986.
[41] A. Talwar, R. Jurca, and B. Faltings, "Understanding User Behavior in Online Feedback Reporting," Proc. ACM Conf. Electronic Commerce, 2007.
[42] K.-L. Tan, P.-K. Eng, and B.C. Ooi, "Efficient Progressive Skyline Computation," Proc. 27th Int'l Conf. Very Large Data Bases (VLDB), 2001.
[43] Y. Tao, X. Xiao, and J. Pei, "SUBSKY: Efficient Computation of Skylines in Subspaces," Proc. 22nd Int'l Conf. Data Eng. (ICDE), 2006.
[44] R.C.-W. Wong, J. Pei, A.W.-C. Fu, and K. Wang, "Mining Favorable Facets," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2007.
[45] T. Xia and D. Zhang, "Refreshing the Sky: The Compressed Skycube with Efficient Support for Frequent Updates," Proc. ACM SIGMOD, 2006.
[46] Y. Yuan, X. Lin, Q. Liu, W. Wang, J.X. Yu, and Q. Zhang, "Efficient Computation of the Skyline Cube," Proc. 31st Int'l Conf. Very Large Data Bases (VLDB), 2005.
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