NDLI: COM: a generative model for group recommendation

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COM: a generative model for group recommendation

Content Provider	ACM Digital Library
Author	Cong, Gao Lin, Chin-Yew Yuan, Quan
Abstract	With the rapid development of online social networks, a growing number of people are willing to share their group activities, e.g. having dinners with colleagues, and watching movies with spouses. This motivates the studies on group recommendation, which aims to recommend items for a group of users. Group recommendation is a challenging problem because different group members have different preferences, and how to make a trade-off among their preferences for recommendation is still an open problem. In this paper, we propose a probabilistic model named COM (COnsensus Model) to model the generative process of group activities, and make group recommendations. Intuitively, users in a group may have different influences, and those who are expert in topics relevant to the group are usually more influential. In addition, users in a group may behave differently as group members from as individuals. COM is designed based on these intuitions, and is able to incorporate both users' selection history and personal considerations of content factors. When making recommendations, COM estimates the preference of a group to an item by aggregating the preferences of the group members with different weights. We conduct extensive experiments on four datasets, and the results show that the proposed model is effective in making group recommendations, and outperforms baseline methods significantly.
Starting Page	163
Ending Page	172
Page Count	10
File Format	PDF MP4
ISBN	9781450329569
DOI	10.1145/2623330.2623616
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Topic models Group recommendation
Content Type	Audio Text
Resource Type	Article

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