Textual-geographical-social aware point-of-interest recommendation

doi:10.1016/S1005-8885(16)60066-3

中国邮电高校学报(英文) ›› 2016, Vol. 23 ›› Issue (6): 24-33.doi: 10.1016/S1005-8885(16)60066-3

• Artificial Intelligence • 上一篇下一篇

Textual-geographical-social aware point-of-interest recommendation

任星怡

北京邮电大学

收稿日期:2016-06-16 修回日期:2016-09-22 出版日期:2016-12-31 发布日期:2016-12-30
通讯作者: 任星怡 E-mail:xyren@bupt.edu.cn

Textual-geographical-social aware point-of-interest recommendation

Xing-Yi REN

Received:2016-06-16 Revised:2016-09-22 Online:2016-12-31 Published:2016-12-30
Contact: Xing-Yi REN E-mail:xyren@bupt.edu.cn

摘要/Abstract

摘要： The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through point-of-interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model (PMF) to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

关键词: location-based social networks, POI recommendation, topic model, geographical correlations, social correlations

Abstract: The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through point-of-interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model (PMF) to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

Key words: location-based social networks, POI recommendation, topic model, geographical correlations, social correlations

Xing-Yi REN,Song Meina, E Haihong, Song Junde. Textual-geographical-social aware point-of-interest recommendation[J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2016, 23(6): 24-33.

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Textual-geographical-social aware point-of-interest recommendation

Textual-geographical-social aware point-of-interest recommendation

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