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Unified Group Recommendation Towards Multiple Criteria

  • Yi Wu
  • Ning YangEmail author
  • Huanrui Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)

Abstract

In online social networks, a growing number of people are willing to share their activities with ones who have common interests. This motivates the research on group recommendation, which focuses on the issue of recommending items to a group of users. The existing methods on addressing the problem of grouping users and making recommendations for the formed groups simultaneously, however, often suffer from two defects. The first one is that they separate group partition and group recommendation, which often reduce the overall group satisfaction. The second one is that they tend to pursue a single objective optimum instead of making a balance between multiple objectives.

In this paper, we strive to tackle the key problem of grouping users and making recommendations for the formed groups simultaneously. It is a challenging problem due to the differences between user preferences over items, and how to make a trade-off among their preferences for the recommended items is still the main research point. To address these challenges, we present a Unified Group Recommendation (UGR) model, which intertwines the user grouping and group recommendation in a unified multi-objective optimization process that makes a balance between multiple criteria, including maximizing overall group satisfaction, social relationship density, and overall group fairness. Extensive experiments on two real-world datasets verify the effectiveness of our method.

Keywords

Group partition Group recommendation Multi-objective optimization 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer ScienceSichuan UniversityChengduChina

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