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On successive point-of-interest recommendation

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Abstract

With the increasing popularity of location-based social networks (LBSNs), users are able to share the Point-of-Interests (POIs) they visited by check-ins. By analyzing the users’ historical check-in records, POI recommendation can help users get better visiting experience by recommending POIs which users may be interested in. Although recent successive POI recommendation methods consider geographical influence by measuring the distances among POIs, most of them ignore the influence of the regions where the POIs are located. Therefore, we propose in this paper two models to tackle the problem of successive POI recommendation. First, a feature-based successive POI recommendation method, named UGSE-LR, is proposed to take the influence of regions, named regional influence, into consideration when recommending POIs. UGSE-LR first splits an area into grids for estimating regional influence. Then, UGSE-LR applies Edge-weighted Personalized PageRank (EdgePPR) for modeling the successive transitions among POIs. Finally, UGSE-LR fuses user preference, regional influence and successive transition influence into a unified recommendation framework. In addition, with the aid of Recurrent Neural Network (RNN), we propose a latent-factor based successive POI recommendation method, named PEU-RNN, to integrate the sequential visits of POIs and user preference to recommend POIs. First, PEU-RNN adopts the word embedding technique to transform each POI into a latent vector. Then, RNN is used to recommend the POIs depend on the users’ historical check-in records. Experimental results on two real LBSN datasets show that our methods are more accurate than the state-of-the-art successive POI recommendation methods in terms of precision and recall. In addition, experimental results also show that PEU-RNN is suitable for the datasets with many check-in records, while UGSE-LR is suitable for the datasets with moderate check-in records.

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Notes

  1. Stanford Network Analysis Project (SNAP): http://snap.stanford.edu/data/index.html.

  2. https://techcrunch.com/2010/01/28/gowalla-opens-trips-to-all-a-simple-way-to-organize-pub-crawls/

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Acknowledgements

This work was supported in part by Ministry of Science and Technology, Taiwan, under contracts 105-2221-E-009-160 and 105-2218-E-009-011.

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  1. Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan

    Yi-Shu Lu, Wen-Yueh Shih, Hung-Yi Gau, Kuan-Chieh Chung & Jiun-Long Huang

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Correspondence to Jiun-Long Huang.

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This article belongs to the Topical Collection: Special Issue on Social Media and Interactive Technologies

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Lu, YS., Shih, WY., Gau, HY. et al. On successive point-of-interest recommendation. World Wide Web 22, 1151–1173 (2019). https://doi.org/10.1007/s11280-018-0599-5

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