Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs
Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-02-01
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| Online Access: | http://www.mdpi.com/2078-2489/8/1/20 |
| _version_ | 1811267659836162048 |
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| author | Lei Guo Haoran Jiang Xinhua Wang Fangai Liu |
| author_facet | Lei Guo Haoran Jiang Xinhua Wang Fangai Liu |
| author_sort | Lei Guo |
| collection | DOAJ |
| description | Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method. |
| first_indexed | 2024-04-12T21:06:45Z |
| format | Article |
| id | doaj.art-ff7f964a288b4dda8a319abe03414e52 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-04-12T21:06:45Z |
| publishDate | 2017-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ff7f964a288b4dda8a319abe03414e522022-12-22T03:16:41ZengMDPI AGInformation2078-24892017-02-01812010.3390/info8010020info8010020Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNsLei Guo0Haoran Jiang1Xinhua Wang2Fangai Liu3School of Management Science and Engineering, Shandong Normal University, Jinan 250014, ChinaInformation Technology Bureau of Shandong Province, China Post Group, Jinan 250001, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250014, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250014, ChinaPoint-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.http://www.mdpi.com/2078-2489/8/1/20point-of-interestlocation recommendationLBSNs |
| spellingShingle | Lei Guo Haoran Jiang Xinhua Wang Fangai Liu Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs Information point-of-interest location recommendation LBSNs |
| title | Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs |
| title_full | Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs |
| title_fullStr | Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs |
| title_full_unstemmed | Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs |
| title_short | Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs |
| title_sort | learning to recommend point of interest with the weighted bayesian personalized ranking method in lbsns |
| topic | point-of-interest location recommendation LBSNs |
| url | http://www.mdpi.com/2078-2489/8/1/20 |
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