GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Let's browse: a collaborative Web browsing agent
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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In recent years, recommender systems have achieved a great success. Popular sites like Amazon.com and CDNow give thousands of recommendations every day. However, although many activities are carried out in groups, like going to the theater with friends, these systems are focused on recommending items for individual users. This brings out the need of systems capable of performing recommendations for groups of people, a domain that has received little attention in the literature. In this article we introduce an investigation of automatic group recommendations, making connections with problems considered in social choice and psychology. Then we suggest a novel method of making recommendations for groups, based on existing techniques of collaborative filtering and classification of alternatives using fuzzy majority. Finally we experimentally evaluate the proposed method to see its behavior under groups of different sizes and degrees of homogeneity.