過去在推薦系統(recommender systems)的研究上，主要針對單一的使用者(user)進行推薦。經由個人過去的消費模式，學習其興趣與嗜好，使系統在未來能為使用者在消費前進行個人化的推薦。但許多的消費行為，例如電影、餐廰用餐或旅遊等等，常常皆是與親朋好友群體(group)共同進行的活動。個人化的推薦並無法滿足群體的需求，而過去在群體推薦的研究上，群體推薦的產生大部份皆是以群體中，每位成員個人的評比(rating)為基礎，合併而成的結果。此結果乎略了群體成員間不同的地位與人格特質，在群體互動時對群體決策的影響，因此無法反應出每個成員在群體決策制定時不同的影響力。
本文所介紹的群體推薦演算法是以群體的評比為基礎，可反應群體決策時群體成員間的互動關係。且若群體評比資料稀疏時，可利用基因演算法(Genetic Algorithms)訓練出群體中每位成員間決策時的強弱關係，以預測此群體對於相似商品的喜好度，藉此彌補資料稀疏的問題，增加群體推薦的準確度。實驗評估的結果在資料充足的情況，不使用基因演算法進行預測時即有不錯的準確度。而在資料稀疏的情況下，使用基因演算法進行預測確實可增加預測的準確度。
群體推薦的結果能做為群體決策時的一個參考，更能滿足一般使用者在從事群體活動時的需求，此為一般個人化的推薦系統所無法逹到的要求。

Ardissono, L., Goy, A., Petrone, G., Segnan, M., and Torasso, P. INTRIGUE: Personalized recommendation of tourist attractions for desktop and handset devices. Applied Arti_cial Intelligence, 17(8-9):687.714, 2003.
Armstrong, R., Freitag, D., Joachims, T., and Mitchell, T. WebWatcher: A Learning Apprentice for the World Wide Web. In Proceedings of the AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, Stanford, CA, 1995.
Asch, S. E. Forming Impressions of Personality. Journal of Abnormal and Social Psychology, 41, 258-290, 1946.
Balabanovic, M. and Shoham, Y. Fab: Content-Based, Collaborative Recommendation. Communications of ACM, 40(3), 66-72, 1997.
Bonner, H. Group dynamics: Principles and applications, New York: Ronald, 1959.
Breese, J. S., Heckerman, D., and Kadie, C. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. (UAI-98), pages 43-52 San Francisco, July 24-26, 1998.
Cartwright, D. and Zander, A. (eds.). Group dynamics: Research and thory,(3rd ed.) New York: Harper & Row, 1968.
Claypool, M. and Gokhale, A. Combining Content-based and Collaborative Filters in an Online Newspaper. Workshop on Recommender System: Algorithms and Evaluation, 1999.
Delgado, J., Ishii, N., and Ura, T. Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents, In Proc. Second Int. Workshop, CIA’98, 1998.
Forsyth, D. R. “A psychological perspective on ethical uncertainties in behavioral research”. In A. J. Kimmel(ed.), New Directions for Methodology of Social and Behavioral Science: Ethics of Human Subject Research (No 10), San Francisco: Jossey-Bass, 1981.
Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61-70, December 1992.
Hare, A. P. Handbook of small group research (2nd ed.), New York: Free Press, 1976.
Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedll, J. An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of the 1999 Conference on Research and Development in Information Retrieval, Aug. 1999.
Hill, W., Stead, L., Rosentein, M., and Furnas, G. W. Recommending and evaluating choices in a virtual community of use. In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems. ACM, New York, 194–201, 1995.
Holland, J. H. Adaptation in Natural and Artificial Systems, Ann Arbor:The University of Michigan Press, 1975.
Hollander, E. P. Principles and methods of social psychology (2nd ed.), New York: Oxford University Press, 1971.
Homans, G. C. The human group, New York: Harcourt, Brace, & World, 1950.
Jameson, A. More Than the Sum of Its Members: Challenges for Group Recommender Systems, 2004.
Krulwich, B. and Burkey, C. The InfoFinder agent: Learning user intereststhrough heuristic phrase extraction. IEEE Intelligent Systems Journal (Expert), vol.12, no. 5, pp. 22-27, 1997.
Lang, K. NewsWeeder: Learning to filter netnews. In Proceedings of the Twelfth International Conference on Machine Learning, pp. 331--339 San Francisco, CA. Morgan Kaufman, 1995.
Lawrence, R.D., Almasi, G.S., Kotlyar, V., Viveros, M.S., and Duri., S.S. “Personalization of Supermarket Product Recommendations,” Data Mining and Knowledge Discovery, vol. 5, no. 1-2, pp. 11-32, 2001.
McCarthy, J. F. and Anagnost, T. D. MusicFX: An arbiter of group preferences for computer supported collaborative workouts. In Proceedings of the 1998 Conference on Computer-Supported Cooperative Work, pages 363.372, 1998.
O''Connor, M., Cosley, D., Konstan, J., and Riedl, J. PolyLens:A recommender system for groups of users. In Proceedings of the European Conference on Computer-Supported Cooperative Work, 2001.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM CSCW’94 Conference on Computer-Supported Cooperative Work, Pages 175-186, 1994.
Rowe, A. J. and Boulgarides, J. D. “Managerial Decision Making”, Macmillan Publishing Company, 1992.
Shardanand, U. and Maes, P. Social information filtering: Algorithms for automating “wordof mouth”. In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems.ACM, New York. 210–217, 1995.
Shaw, M. E. Group dynamics: The Psychology of small group behavior (3rd ed.)., McGraw-Hill, 1981.
Torrance, E. P. “Some Consequences of power difference on decision making in permanent and temporary three-man groups”. In A. P. Hare, E. F. Bogatta, and R. F. Bales (Eds), Small groups: Studies in Social interaction, New York: Knopf, 1955.
Wasfi, A. M. A.. Collecting User Access Patterns for Building user Profiles and Collaborative Filtering, In Int. Conf. On Intelligent User Interfaces. 1999.
Michael Argyle著，陸洛譯，社會行為之科學研究，民85
丁興祥 李美枝 陳皎眉，社會心理學，民84
張春興，心理學，民87