On successive point-of-interest recommendation

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

*Corresponding author for this work

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)1151-1173
頁數23
期刊World Wide Web
22
發行號3
DOIs
出版狀態Published - 15 五月 2019

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