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World Wide Web

Spatiotemporal data management and analytics for recommender systems

  • 13-03-2023
  • Editorial
Published in:
Authors
Shuo Shang
Xiangliang Zhang
Panos Kalnis
Published in
World Wide Web | Issue 5/2023

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Excerpt

This special issue compiles 22 research articles and 2 surveys that delve into the advanced field of spatiotemporal data analytics for recommender systems. The articles cover a wide range of topics, including trajectory prediction using Graph Neural Networks and BiLSTM, light-weighted indexing structures for continuous location-based query processing, and personalized POI recommendations. Notably, the papers introduce novel frameworks such as PreCLN for vehicle trajectory prediction, HMGCL for capturing human mobility patterns, and CSQKM for efficient spatial keyword query matching. Additionally, the surveys provide a systematic review of existing techniques in ocean spatiotemporal data processing and machine learning applications in marine hydrometeorology. This collection offers a wealth of insights and innovations that are poised to advance the field of spatiotemporal data management and analytics, making it a must-read for professionals and researchers in the domain.
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Title
Spatiotemporal data management and analytics for recommender systems
Authors
Shuo Shang
Xiangliang Zhang
Panos Kalnis
Publication date
13-03-2023
Publisher
Springer US
Published in
World Wide Web / Issue 5/2023
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-023-01162-2

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