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Group-Based Recurrent Neural Networks for POI Recommendation

Published:12 March 2020Publication History
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Abstract

With the development of mobile Internet, many location-based services have accumulated a large amount of data that can be used for point-of-interest (POI) recommendation. However, there are still challenges in developing an unified framework to incorporate multiple factors associated with both POIs and users due to the heterogeneity and implicity of this information. To alleviate the problem, this work proposes a novel group-based method for POI recommendation jointly considering the reviews, categories, and geographical locations, called the Group-based Temporal Sentiment-Aspect-Region Recurrent Neural Network (GTSAR-RNN). We divide the users into different groups and then train an individual RNN for each group with the goal of improving its pertinence. In GTSAR-RNN, we consider not only the effects of temporal and geographical contexts but also the users’ sentimental opinions on locations. Experimental results show that GTSAR-RNN acquires significant improvements over the baseline methods on real datasets.

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    • Published in

      cover image ACM/IMS Transactions on Data Science
      ACM/IMS Transactions on Data Science  Volume 1, Issue 1
      February 2020
      159 pages
      ISSN:2691-1922
      DOI:10.1145/3388324
      Issue’s Table of Contents

      Copyright © 2020 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 March 2020
      • Accepted: 1 February 2019
      • Revised: 1 November 2018
      • Received: 1 July 2018
      Published in tds Volume 1, Issue 1

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