Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
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GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
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Knowledge-Based Systems
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Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
On the design of individual and group recommender systems for tourism
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A collaborative filtering similarity measure based on singularities
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Information Processing and Management: an International Journal
Knowledge-Based Systems
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In collaborative filtering recommender systems recommendations can be made to groups of users. There are four basic stages in the collaborative filtering algorithms where the group's users' data can be aggregated to the data of the group of users: similarity metric, establishing the neighborhood, prediction phase, determination of recommended items. In this paper we perform aggregation experiments in each of the four stages and two fundamental conclusions are reached: (1) the system accuracy does not vary significantly according to the stage where the aggregation is performed, (2) the system performance improves notably when the aggregation is performed in an earlier stage of the collaborative filtering process. This paper provides a group recommendation similarity metric and demonstrates the convenience of tackling the aggregation of the group's users in the actual similarity metric of the collaborative filtering process.