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Recommendation in a Changing World: Exploiting Temporal Dynamics in Ratings and Reviews

Published:21 August 2017Publication History
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Abstract

Users’ preferences, and consequently their ratings and reviews to items, change over time. Likewise, characteristics of items are also time-varying. By dividing data into time periods, temporal Recommender Systems (RSs) improve recommendation accuracy by exploring the temporal dynamics in user rating data. However, temporal RSs have to cope with rating sparsity in each time period. Meanwhile, reviews generated by users contain rich information about their preferences, which can be exploited to address rating sparsity and further improve the performance of temporal RSs. In this article, we develop a temporal rating model with topics that jointly mines the temporal dynamics of both user-item ratings and reviews. Studying temporal drifts in reviews helps us understand item rating evolutions and user interest changes over time. Our model also automatically splits the review text in each time period into interim words and intrinsic words. By linking interim words and intrinsic words to short-term and long-term item features, respectively, we jointly mine the temporal changes in user and item latent features together with the associated review text in a single learning stage. Through experiments on 28 real-world datasets collected from Amazon, we show that the rating prediction accuracy of our model significantly outperforms the existing state-of-art RS models. And our model can automatically identify representative interim words in each time period as well as intrinsic words across all time periods. This can be very useful in understanding the time evolution of users’ preferences and items’ characteristics.

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    • Published in

      cover image ACM Transactions on the Web
      ACM Transactions on the Web  Volume 12, Issue 1
      February 2018
      169 pages
      ISSN:1559-1131
      EISSN:1559-114X
      DOI:10.1145/3133955
      Issue’s Table of Contents

      Copyright © 2017 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 21 August 2017
      • Accepted: 1 June 2017
      • Revised: 1 March 2017
      • Received: 1 December 2015
      Published in tweb Volume 12, Issue 1

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