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Understanding Human Mobility from Temporal Perspective

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

Abstract

Understanding user mobility from the temporal perspective is the key to POI recommendation that mines user check-in sequences to suggest interesting locations for users. Because user mobility in LBSNs exhibits strong temporal patterns—for instance, users would like to check-in at restaurants at noon and visit bars at night. Hence, capturing the temporal influence is necessary to ensure the high performance in a POI recommendation system. This chapter summarizes the temporal characteristics of user mobility in LBSNs in three aspects: periodicity, consecutiveness, and non-uniformness. Moreover, an Aggregated Temporal Tensor Factorization (ATTF) model for POI recommendation is proposed to capture the three temporal features. Experiments on two real-world datasets show that the ATTF model achieves better performance than the state-of-the-art temporal models for POI recommendation.

Keywords

POI recommendation Temporal influence Tensor factorization 

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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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