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Local Variational Feature-Based Similarity Models for Recommending Top-N New Items

Published: 11 February 2020 Publication History

Abstract

The top-N recommendation problem has been studied extensively. Item-based collaborative filtering recommendation algorithms show promising results for the problem. They predict a user’s preferences by estimating similarities between a target and user-rated items. Top-N recommendation remains a challenging task in scenarios where there is a lack of preference history for new items. Feature-based Similarity Models (FSMs) address this particular problem by extending item-based collaborative filtering by estimating similarity functions of item features. The quality of the estimated similarity function determines the accuracy of the recommendation. However, existing FSMs only estimate global similarity functions; i.e., they estimate using preference information across all users. Moreover, the estimated similarity functions are linear; hence, they may fail to capture the complex structure underlying item features.
In this article, we propose to improve FSMs by estimating local similarity functions, where each function is estimated for a subset of like-minded users. To capture global preference patterns, we extend the global similarity function from linear to nonlinear, based on the effectiveness of variational autoencoders. We propose a Bayesian generative model, called the Local Variational Feature-based Similarity Model, to encapsulate local and global similarity functions. We present a variational Expectation Minimization algorithm for efficient approximate inference. Extensive experiments on a large number of real-world datasets demonstrate the effectiveness of our proposed model.

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cover image ACM Transactions on Information Systems
ACM Transactions on Information Systems  Volume 38, Issue 2
April 2020
266 pages
ISSN:1046-8188
EISSN:1558-2868
DOI:10.1145/3379433
Issue’s Table of Contents
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Published: 11 February 2020
Accepted: 01 November 2019
Revised: 01 October 2019
Received: 01 December 2018
Published in TOIS Volume 38, Issue 2

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Author Tags

  1. Top-N recommendation
  2. deep generative model
  3. item cold-start
  4. item feature

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  • Netherlands Institute for Sound and Vision
  • Netherlands Organisation for Scientific Research (NWO)
  • China Scholarship Council
  • Ahold Delhaize
  • National Natural Science Foundation of China
  • Natural Science Foundation of Hunan
  • Association of Universities in the Netherlands (VSNU)

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