Flytrap: intelligent group music recommendation
Proceedings of the 7th international conference on Intelligent user interfaces
A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit personalization of public environments using bluetooth
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Collaborative filtering with temporal dynamics
Communications of the ACM
The adaptive web
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This paper presents an algorithm capable of providing meaningful recommendations to small sets of users. We consider not only rating patterns, bias tendencies, and temporal fluctuations, but also group-leaders. The approach here presented intends to bring a fresh new look over group recommendations, making use of latent factor space to identify groups and make recommendations. Although these recommendations are oriented towards a few users, the preferences of their respective group leaders (users that better represent the group) are also taken into account to diversify and smooth these recommendations. In contrast to the majority of group recommender systems described in literature, our system employs a collaborative filtering approach based on latent factor space instead of content-based or ratings merging approaches.