SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation
Abstract The expansion of available information in location-based social networks (LBSNs) has led to information overload, making it urgent to discover users’ next point-of-interest (POI). Some existing works only consider certain modal information in LBSNs and do not transform them into high-dimens...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-08-01
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| Series: | Data Science and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41019-023-00221-y |
| _version_ | 1797659845532319744 |
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| author | Junzhuang Wu Yujing Zhang Yuhua Li Yixiong Zou Ruixuan Li Zhenyu Zhang |
| author_facet | Junzhuang Wu Yujing Zhang Yuhua Li Yixiong Zou Ruixuan Li Zhenyu Zhang |
| author_sort | Junzhuang Wu |
| collection | DOAJ |
| description | Abstract The expansion of available information in location-based social networks (LBSNs) has led to information overload, making it urgent to discover users’ next point-of-interest (POI). Some existing works only consider certain modal information in LBSNs and do not transform them into high-dimensional structures, which hinders the alleviation of the data sparsity problem. Moreover, many approaches rely solely on social relationships, making it difficult to recommend POIs to new users without association information. To tackle these challenges, we propose a social- and spatial–temporal-aware next point-of-Interest (SSTP) recommendation model. SSTP uses two feature encoders based on self-attention mechanism and gate recurrent unit to model users’ check-in enhancement sequence hierarchically. We also design a random neighborhood sampling approach to mine user social relationships, thus alleviating the user cold start problem. Finally, we propose a geographical-aware graph attention network to learn the sensitivity of users to distance. Extensive experiments on two real-world datasets show that SSTP outperforms state-of-the-art models, improving Hit@k by 2.26–6.55 $$\%$$ % and MAP@k by 3.49–6.55 $$\%$$ % . Moreover, SSTP has better performance on sparse data, with an average improvement of 6.09 $$\%$$ % on the Hit@k. The code can be downloaded at https://github.com/Rih0/sstp . |
| first_indexed | 2024-03-11T18:21:05Z |
| format | Article |
| id | doaj.art-65422311aa8d4f6ba05b60e539ca264e |
| institution | Directory Open Access Journal |
| issn | 2364-1185 2364-1541 |
| language | English |
| last_indexed | 2024-03-11T18:21:05Z |
| publishDate | 2023-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj.art-65422311aa8d4f6ba05b60e539ca264e2023-10-15T11:24:41ZengSpringerOpenData Science and Engineering2364-11852364-15412023-08-018432934310.1007/s41019-023-00221-ySSTP: Social and Spatial-Temporal Aware Next Point-of-Interest RecommendationJunzhuang Wu0Yujing Zhang1Yuhua Li2Yixiong Zou3Ruixuan Li4Zhenyu Zhang5School of Computer Science and Technology, Huazhong University of Science and TechnologySchool of Computer Science and Technology, Huazhong University of Science and TechnologySchool of Computer Science and Technology, Huazhong University of Science and TechnologySchool of Computer Science and Technology, Huazhong University of Science and TechnologySchool of Computer Science and Technology, Huazhong University of Science and TechnologySchool of Computer Science and Technology, Huazhong University of Science and TechnologyAbstract The expansion of available information in location-based social networks (LBSNs) has led to information overload, making it urgent to discover users’ next point-of-interest (POI). Some existing works only consider certain modal information in LBSNs and do not transform them into high-dimensional structures, which hinders the alleviation of the data sparsity problem. Moreover, many approaches rely solely on social relationships, making it difficult to recommend POIs to new users without association information. To tackle these challenges, we propose a social- and spatial–temporal-aware next point-of-Interest (SSTP) recommendation model. SSTP uses two feature encoders based on self-attention mechanism and gate recurrent unit to model users’ check-in enhancement sequence hierarchically. We also design a random neighborhood sampling approach to mine user social relationships, thus alleviating the user cold start problem. Finally, we propose a geographical-aware graph attention network to learn the sensitivity of users to distance. Extensive experiments on two real-world datasets show that SSTP outperforms state-of-the-art models, improving Hit@k by 2.26–6.55 $$\%$$ % and MAP@k by 3.49–6.55 $$\%$$ % . Moreover, SSTP has better performance on sparse data, with an average improvement of 6.09 $$\%$$ % on the Hit@k. The code can be downloaded at https://github.com/Rih0/sstp .https://doi.org/10.1007/s41019-023-00221-yRecommendation systemsLocation-based social networksPoint-of-interestAttention mechanismGraph attention network |
| spellingShingle | Junzhuang Wu Yujing Zhang Yuhua Li Yixiong Zou Ruixuan Li Zhenyu Zhang SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation Data Science and Engineering Recommendation systems Location-based social networks Point-of-interest Attention mechanism Graph attention network |
| title | SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation |
| title_full | SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation |
| title_fullStr | SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation |
| title_full_unstemmed | SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation |
| title_short | SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation |
| title_sort | sstp social and spatial temporal aware next point of interest recommendation |
| topic | Recommendation systems Location-based social networks Point-of-interest Attention mechanism Graph attention network |
| url | https://doi.org/10.1007/s41019-023-00221-y |
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