ABSTRACT
Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.
Supplemental Material
- }}L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso. Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices. Applied Artificial Intelligence, 17(8-9):687--714, 2003.Google ScholarCross Ref
- }}S. Berkovsky, J. Freyne, and M. Coombe. Aggregation trade offs in family based recommendations. In Australasian Conf. on Artificial Intelligence, 2009. Google ScholarDigital Library
- }}S. Berkovsky, T. Kuflik, and F. Ricci. Distributed collaborative filtering with domain specialization. In Conf. on Recommender Systems, 2007. Google ScholarDigital Library
- }}P. Brusilovsky, G. Chavan, and R. Farzan. Social adaptive navigation support for open corpus electronic textbooks. In Int. Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems, 2004.Google ScholarCross Ref
- }}R. D. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331--370, 2002. Google ScholarDigital Library
- }}Y.-L. Chen, L.-C. Cheng, and C.-N. Chuang. A group recommendation system with consideration of interactions among group members. Expert Systems with Applications, 34(3):2082--2090, 2008. Google ScholarDigital Library
- }}J. Freyne and B. Smyth. Cooperating search communities. In Int. Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems, 2006. Google ScholarDigital Library
- }}J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. Transactions on Information Systems, 22(1):5--53, 2004. Google ScholarDigital Library
- }}A. Jameson and B. Smyth. Recommendation to groups. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web: Methods and Strategies of Web Personalization. Springer, 2007. Google ScholarDigital Library
- }}J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl. GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM, 40(3):77--87, 1997. Google ScholarDigital Library
- }}J. Masthoff. Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction, 14(1):37--85, 2004. Google ScholarDigital Library
- }}J. F. McCarthy and T. D. Anagnost. Musicfx: An arbiter of group preferences for computer supported collaborative workouts. In Int. Conf. on Computer Supported Collaborative Work, 1998. Google ScholarDigital Library
- }}K. McCarthy, L. McGinty, and B. Smyth. Case-based group recommendation: Compromising for success. In Int. Conf. on Case Based Reasoning, 2007. Google ScholarDigital Library
- }}M. Noakes and P. Clifton. The CSIRO Total Wellbeing Diet Book. Penguin, 2005.Google Scholar
- }}M. O'Connor, D. Cosley, J. A. Konstan, and J. Riedl. Polylens: a recommender system for groups of users. In European Conf. on Computer Supported Cooperative Work, 2001. Google ScholarDigital Library
- }}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, 16(1):63--82, 2006. Google ScholarDigital Library
Index Terms
- Group-based recipe recommendations: analysis of data aggregation strategies
Recommendations
Group recommendations with rank aggregation and collaborative filtering
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Hybrid group recommendations for a travel service
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A study on the role of uninterested items in group recommendations
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