Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine
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 conte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/4/258 |
| _version_ | 1797538048392560640 |
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| author | Dongjin Yu Yi Shen Kaihui Xu Yihang Xu |
| author_facet | Dongjin Yu Yi Shen Kaihui Xu Yihang Xu |
| author_sort | Dongjin Yu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:24:55Z |
| format | Article |
| id | doaj.art-3b00d39d1e4846679430734620c45a8d |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T12:24:55Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-3b00d39d1e4846679430734620c45a8d2023-11-21T15:07:04ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-04-0110425810.3390/ijgi10040258Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization MachineDongjin Yu0Yi Shen1Kaihui Xu2Yihang Xu3School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaPoint-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.https://www.mdpi.com/2220-9964/10/4/258location-based social networkcontext-specificpoint-of-interest recommendationheterogeneous information networkweighted random samplingFactorization Machine |
| spellingShingle | Dongjin Yu Yi Shen Kaihui Xu Yihang Xu Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine ISPRS International Journal of Geo-Information location-based social network context-specific point-of-interest recommendation heterogeneous information network weighted random sampling Factorization Machine |
| title | Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine |
| title_full | Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine |
| title_fullStr | Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine |
| title_full_unstemmed | Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine |
| title_short | Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine |
| title_sort | context specific point of interest recommendation based on popularity weighted random sampling and factorization machine |
| topic | location-based social network context-specific point-of-interest recommendation heterogeneous information network weighted random sampling Factorization Machine |
| url | https://www.mdpi.com/2220-9964/10/4/258 |
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