An Effective Approach to Group Recommendation Based on Belief Propagation

Title
An Effective Approach to Group Recommendation Based on Belief Propagation
Authors
김상욱
Keywords
Recommender systems; group recommendation; belief propagation
Issue Date
2015-04
Publisher
ACM SAC
Citation
Proceedings of the 30th Annual ACM Symposium on Applied Computing, Pages 1148-1153
Abstract
Recommender systems have been an active research topic for the past decade. Previous studies have primarily focused on recommendations to a single user. Recently, several interesting approaches to make group recommendations have been proposed. However, the accuracy of existing approaches is significantly affected by the size and cohesiveness of a group. In this paper, we present a novel approach that makes effective group recommendations regardless of the group size or cohesiveness. We first model the relationships between a set of users and a set of items as a bipartite graph from the ratings information. On this graph, we employ the belief propagation to determine probabilistically the target user group’s preference on items. We also propose a new group type that reflects real-life groups effectively and helps better evaluation of group recommendation approaches. Through extensive experiments on a real-life data set, we show that the proposed approach is more accurate than the existing ones up to 20%.
URI
http://dl.acm.org/citation.cfm?doid=2695664.2695840http://hdl.handle.net/20.500.11754/24277
ISBN
978-1-4503-3196-8
DOI
http://dx.doi.org/10.1145/2695664.2695840
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > DIVISION OF COMPUTER SCIENCES AND ENGINEERING(컴퓨터공학부) > Articles
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