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Folkommender: a group recommender system based on a graph-based ranking algorithm

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Abstract

With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a new graph-based approach to a recommender system, called Folkommender, that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on two real-world datasets demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.

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Notes

  1. A generally accepted value for d is 0.15.

  2. The dataset can be downloaded from http://www.grouplens.org/node/73.

  3. In this user study, α was set to 0.7.

  4. http://www.samsung.com/us/appstore/.

  5. http://www.google.com/tv/.

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  1. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, Ottawa, ON, K1N 6N5, Canada

    Heung-Nam Kim, Mark Bloess & Abdulmotaleb El Saddik

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  1. Heung-Nam Kim
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  2. Mark Bloess
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  3. Abdulmotaleb El Saddik
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Correspondence to Heung-Nam Kim.

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Kim, HN., Bloess, M. & El Saddik, A. Folkommender: a group recommender system based on a graph-based ranking algorithm. Multimedia Systems 19, 509–525 (2013). https://doi.org/10.1007/s00530-012-0298-5

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