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Article

Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
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Academic Editors: Georg Gartner and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 258; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040258
Received: 30 January 2021 / Revised: 31 March 2021 / Accepted: 4 April 2021 / Published: 11 April 2021
Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user’s home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others. View Full-Text
Keywords: location-based social network; context-specific; point-of-interest recommendation; heterogeneous information network; weighted random sampling; Factorization Machine location-based social network; context-specific; point-of-interest recommendation; heterogeneous information network; weighted random sampling; Factorization Machine
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MDPI and ACS Style

Yu, D.; Shen, Y.; Xu, K.; Xu, Y. Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine. ISPRS Int. J. Geo-Inf. 2021, 10, 258. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040258

AMA Style

Yu D, Shen Y, Xu K, Xu Y. Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine. ISPRS International Journal of Geo-Information. 2021; 10(4):258. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040258

Chicago/Turabian Style

Yu, Dongjin; Shen, Yi; Xu, Kaihui; Xu, Yihang. 2021. "Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine" ISPRS Int. J. Geo-Inf. 10, no. 4: 258. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040258

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