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

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.

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