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

  • Shenglin Zhao
  • Michael R. Lyu
  • Irwin King
Chapter
Part of the SpringerBriefs in Computer Science book series (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.

Keywords

Successive POI recommendation Geographical influence Temporal influence Latent ranking 

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Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Youtu LabTencentShenzhenChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina

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