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STELLAR: Spatial-Temporal Latent Ranking Model for Successive POI Recommendation

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Point-of-Interest Recommendation in Location-Based Social Networks

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

Successive POI recommendation in LBSNs becomes a significant task since it helps users to navigate a large number of candidate POIs and provide the best POI recommendations based on users’ most recent check-in knowledge. However, all existing methods for successive POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences, but ignore an important fact that successive POI recommendation is a time-subtle recommendation task. In fact, even with the same previous check-in information, users would prefer different successive POIs at different time. To capture the impact of time on successive POI recommendation, this chapter proposes a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. In particular, the proposed STELLAR model is built upon a ranking-based pairwise tensor factorization framework with a fine-grained modeling of user-POI, POI-time, and POI-POI interactions for successive POI recommendation. Evaluations on two real-world datasets demonstrate that the STELLAR model outperforms state-of-the-art successive POI recommendation model about 20% in Precision@5 and Recall@5.

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Authors and Affiliations

  1. Youtu Lab, Tencent, Shenzhen, Guangdong, China

    Shenglin Zhao

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Michael R. Lyu & Irwin King

Authors
  1. Shenglin Zhao
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  2. Michael R. Lyu
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  3. Irwin King
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Correspondence to Shenglin Zhao .

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Zhao, S., Lyu, M.R., King, I. (2018). STELLAR: Spatial-Temporal Latent Ranking Model for Successive POI Recommendation. In: Point-of-Interest Recommendation in Location-Based Social Networks. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-1349-3_5

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  • DOI: https://doi.org/10.1007/978-981-13-1349-3_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1348-6

  • Online ISBN: 978-981-13-1349-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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