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Conformative Filtering for Implicit Feedback Data

  • Farhan KhawarEmail author
  • Nevin L. Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user “taste” groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines.

Keywords

Implicit feedback One class collaborative filtering Recommender systems 

Notes

Acknowledgment

Research on this article was supported by Hong Kong Research Grants Council under grant 16202118.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyClear Water BayHong Kong

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