A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment
The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/23/3977 |
| _version_ | 1797463371988074496 |
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| author | Xuebo Liu Jingjing Guo Peng Qiao |
| author_facet | Xuebo Liu Jingjing Guo Peng Qiao |
| author_sort | Xuebo Liu |
| collection | DOAJ |
| description | The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN. |
| first_indexed | 2024-03-09T17:50:40Z |
| format | Article |
| id | doaj.art-b39a3807eebc45af809046f2c57d4e62 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:50:40Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b39a3807eebc45af809046f2c57d4e622023-11-24T10:48:37ZengMDPI AGElectronics2079-92922022-11-011123397710.3390/electronics11233977A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT EnvironmentXuebo Liu0Jingjing Guo1Peng Qiao2Shanxi Information Industry Technology Research Institute Co., Ltd., Taiyuan 030012, ChinaShanxi Information Industry Technology Research Institute Co., Ltd., Taiyuan 030012, ChinaShanxi Information Industry Technology Research Institute Co., Ltd., Taiyuan 030012, ChinaThe rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN.https://www.mdpi.com/2079-9292/11/23/3977context awarenessattention networkdynamic user preferencesnext POI recommendationIoT |
| spellingShingle | Xuebo Liu Jingjing Guo Peng Qiao A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment Electronics context awareness attention network dynamic user preferences next POI recommendation IoT |
| title | A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment |
| title_full | A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment |
| title_fullStr | A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment |
| title_full_unstemmed | A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment |
| title_short | A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment |
| title_sort | context awareness hierarchical attention network for next poi recommendation in iot environment |
| topic | context awareness attention network dynamic user preferences next POI recommendation IoT |
| url | https://www.mdpi.com/2079-9292/11/23/3977 |
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