An Analysis of Group Recommendation Strategies
Shlomo Berkovsky and Jill Freyne
Tasmanian ICT Center, CSIRO, GPO Box 1538, Hobart, Tasmania 7001, Australia
Collaborative filtering recommender systems often suffer from a data sparsity problem, where systems have insufficient data to generate accurate recommendations. To partially resolve this, the use of group aggregated data in the collaborative filtering recommendations process has been suggested. Although group recommendations are typically less accurate than personalized recommendations, they can be more accurate than generic ones, which are the natural fall back when personalized recommendations cannot be generated. This work presents a study that exploits a dataset of recipe ratings from families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models.
-  J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl, “GroupLens: applying collaborative filtering to Usenet news.” Communications of the ACM, Vol.40, No.3, pp. 77-87, 1997.
-  U. Shardanand and P. Maes, “Social information filtering: algorithms for automating ‘word of mouth,’” Int. Conf. on Human Factors in Computing Systems, pp. 210-217, 1995.
-  Z. Huang, H. Chen, and D. Zeng, “Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering,” Trans. on Information Systems, Vol.22, No.1, pp. 116-142, 2004.
-  A. Jameson and B. Smyth, “Recommendation to groups,” P. Brusilovsky, A. Kobsa, and W. Nejdl, eds., The Adaptive Web Methods and Strategies of Web Personalization, pp. 596-627, Springer, 2007.
-  M. Noakes and P. Clifton, “The CSIRO Total Wellbeing Diet Book,” Penguin, 2005.
-  S. Berkovsky, “Decentralized mediation of user models for a better personalization,” Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 404-408, 2006.
-  J. B. Schafer, J. Konstan, and J. Riedl, “Recommender systems in ecommerce,” Int. Conf. on Electronic Commerce, pp. 158-166, 1999.
-  R. D. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, Vol.12, No.4, pp. 331-370, 2002.
-  J. Freyne and B. Smyth, “Cooperating search communities,” Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 101-110, 2006.
-  Z. Yu, X. Zhou, Y. Hao, and J. Gu, “Tv program recommendation for multiple viewers based on user profile merging,” User Modeling and User-Adapted Interaction, Vol.16, No.1, pp. 63-82, 2006.
-  J. Masthoff, “Group modeling: Selecting a sequence of television items to suit a group of viewers,” User Modeling and User-Adapted Interaction, Vol.14, No.1, pp. 37-85, 2004.
-  M. O’Connor, D. Cosley, J. A. Konstan, and J. Riedl, “Polylens: a recommender system for groups of users,” European Conf. on Computer Supported Cooperative Work, pp. 199-218, 2001.
-  C. Senot, D. Kostadinov, M. Bouzid, J. Picault, A. Aghasaryan, and C. Bernier, “Analysis of strategies for building user group profiles,” Int. Conf. on User Modeling, Adaptivity, and Personalization, pp. 40-51, 2010.
-  P. Brusilovsky, G. Chavan, and R. Farzan, “Social adaptive navigation support for open corpus electronic textbooks,” Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 24-33, 2004.
-  S. Berkovsky, J. Freyne, and M. Coombe, “Aggregation trade offs in family based recommendations,” Australasian Conference on Artificial Intelligence, pp. 646-655, 2009.
-  J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” Trans. on Information Systems, Vol.22, No.1, pp. 5-53, 2004.
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