STS: Spatial–Temporal–Semantic Personalized Location Recommendation
The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of sp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/9/538 |
| _version_ | 1797554283604869120 |
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| author | Wenchao Li Xin Liu Chenggang Yan Guiguang Ding Yaoqi Sun Jiyong Zhang |
| author_facet | Wenchao Li Xin Liu Chenggang Yan Guiguang Ding Yaoqi Sun Jiyong Zhang |
| author_sort | Wenchao Li |
| collection | DOAJ |
| description | The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations, we propose a Gaussian process based model for <i>each</i> user to systematically and non-linearly combine temporal and spatial information to predict the user’s displacement from their currently checked-in location to the next one. The locations whose distances to the user’s current checked-in location are the closest to the predicted displacement are recommended. We also propose an enhancement to take into account category information of locations for semantic-aware recommendation. A unified recommendation framework called <i>s</i>patial–<i>t</i>emporal–<i>s</i>emantic (STS) is introduced to combine displacement prediction and the semantic-aware enhancement to provide final top-<i>N</i> recommendation. Extensive experiments over real datasets show that the proposed STS framework significantly outperforms the state-of-the-art location recommendation models in terms of precision and mean reciprocal rank (MRR). |
| first_indexed | 2024-03-10T16:29:45Z |
| format | Article |
| id | doaj.art-815a07f1564e4a2eb04d8e0d28819215 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T16:29:45Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-815a07f1564e4a2eb04d8e0d288192152023-11-20T12:57:48ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-09-019953810.3390/ijgi9090538STS: Spatial–Temporal–Semantic Personalized Location RecommendationWenchao Li0Xin Liu1Chenggang Yan2Guiguang Ding3Yaoqi Sun4Jiyong Zhang5School of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Software, Tsinghua University, Beijing 100085, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaThe rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations, we propose a Gaussian process based model for <i>each</i> user to systematically and non-linearly combine temporal and spatial information to predict the user’s displacement from their currently checked-in location to the next one. The locations whose distances to the user’s current checked-in location are the closest to the predicted displacement are recommended. We also propose an enhancement to take into account category information of locations for semantic-aware recommendation. A unified recommendation framework called <i>s</i>patial–<i>t</i>emporal–<i>s</i>emantic (STS) is introduced to combine displacement prediction and the semantic-aware enhancement to provide final top-<i>N</i> recommendation. Extensive experiments over real datasets show that the proposed STS framework significantly outperforms the state-of-the-art location recommendation models in terms of precision and mean reciprocal rank (MRR).https://www.mdpi.com/2220-9964/9/9/538location recommendationGaussian processspatial–temporal–semanticlocation-based social networkstop-<i>N</i> recommendation |
| spellingShingle | Wenchao Li Xin Liu Chenggang Yan Guiguang Ding Yaoqi Sun Jiyong Zhang STS: Spatial–Temporal–Semantic Personalized Location Recommendation ISPRS International Journal of Geo-Information location recommendation Gaussian process spatial–temporal–semantic location-based social networks top-<i>N</i> recommendation |
| title | STS: Spatial–Temporal–Semantic Personalized Location Recommendation |
| title_full | STS: Spatial–Temporal–Semantic Personalized Location Recommendation |
| title_fullStr | STS: Spatial–Temporal–Semantic Personalized Location Recommendation |
| title_full_unstemmed | STS: Spatial–Temporal–Semantic Personalized Location Recommendation |
| title_short | STS: Spatial–Temporal–Semantic Personalized Location Recommendation |
| title_sort | sts spatial temporal semantic personalized location recommendation |
| topic | location recommendation Gaussian process spatial–temporal–semantic location-based social networks top-<i>N</i> recommendation |
| url | https://www.mdpi.com/2220-9964/9/9/538 |
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