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Geo-Teaser: Geo-Temporal Sequential Embedding Rank for POI Recommendation

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

Abstract

This chapter proposes a Geo-Temporal sequential embedding rank (Geo-Teaser) model for POI recommendation. Inspired by the success of the word2vec framework to model the sequential contexts, a temporal POI embedding model is proposed to learn POI representations under some particular temporal state. The temporal POI embedding model captures the contextual check-in information in sequences and the various temporal characteristics on different days as well. Furthermore, a new way of incorporating the geographical influence into the pairwise preference ranking method through discriminating the unvisited POIs according to geographical information, is employed to develop a geographically hierarchical pairwise preference ranking model. Finally, a unified framework is proposed to recommend POIs combining these two models. Experimental results on two real-life datasets show that the Geo-Teaser model outperforms state-of-the-art models.

Keywords

Poi recommendation Geographical influence Temporal influence Sequential modeling Embedding learning 

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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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