A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors
Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/10/431 |
| _version_ | 1797626679482384384 |
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| author | Sumet Darapisut Komate Amphawan Nutthanon Leelathakul Sunisa Rimcharoen |
| author_facet | Sumet Darapisut Komate Amphawan Nutthanon Leelathakul Sunisa Rimcharoen |
| author_sort | Sumet Darapisut |
| collection | DOAJ |
| description | Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS. |
| first_indexed | 2024-03-11T10:13:37Z |
| format | Article |
| id | doaj.art-3ae6e999c3b041f5b9fb339de4c38b20 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T10:13:37Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-3ae6e999c3b041f5b9fb339de4c38b202023-11-16T10:30:40ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-10-01121043110.3390/ijgi12100431A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting BehaviorsSumet Darapisut0Komate Amphawan1Nutthanon Leelathakul2Sunisa Rimcharoen3Faculty of Informatics, Burapha University, Chonburi 20131, ThailandFaculty of Informatics, Burapha University, Chonburi 20131, ThailandFaculty of Informatics, Burapha University, Chonburi 20131, ThailandFaculty of Informatics, Burapha University, Chonburi 20131, ThailandLocation-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS.https://www.mdpi.com/2220-9964/12/10/431point-of-interest recommendationslink analysiscollaborative filteringdistinctiveness |
| spellingShingle | Sumet Darapisut Komate Amphawan Nutthanon Leelathakul Sunisa Rimcharoen A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors ISPRS International Journal of Geo-Information point-of-interest recommendations link analysis collaborative filtering distinctiveness |
| title | A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors |
| title_full | A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors |
| title_fullStr | A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors |
| title_full_unstemmed | A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors |
| title_short | A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors |
| title_sort | hybrid poi recommendation system combining link analysis and collaborative filtering based on various visiting behaviors |
| topic | point-of-interest recommendations link analysis collaborative filtering distinctiveness |
| url | https://www.mdpi.com/2220-9964/12/10/431 |
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