Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation
Recent study shows that recommendation system not only relys on user’s static preference, but also dynamic preference. Consequently, it leads to the emergence of session-based recommendation. With the development of recurrent neural network, this kind of method can capture representations...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9866059/ |
| _version_ | 1817993367120773120 |
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| author | Quan Li Xinhua Xu Jinjun Liu Guangmin Li |
| author_facet | Quan Li Xinhua Xu Jinjun Liu Guangmin Li |
| author_sort | Quan Li |
| collection | DOAJ |
| description | Recent study shows that recommendation system not only relys on user’s static preference, but also dynamic preference. Consequently, it leads to the emergence of session-based recommendation. With the development of recurrent neural network, this kind of method can capture representations of users’ sequential behaviors from a large number of sessions. However it is prone to spurious dependency problem. Recently, convolutional neural network has also shown its potential in modelling session, especially in extracting complex local pattern of subsequence. Therefore, we propose a hybrid neural model, called SGPD, for learning sequential general pattern and dependency for session-based recommendation. In SGPD, we propose recurrent residual convolution network to extract general pattern of subsequence in a session. Furthermore, the SGPD scans sequence from forward and reverse direction by bidirectional recurrent neural network, and learns sequential dependency of a session. Finally, the objective function is constructed by cross entropy and the model parameters are learned. The experimental results show that the precision rate, recall rate and mean reciprocal ranking of SGPD are greatly improved compared with the state-of-art methods. It has good application prospect. |
| first_indexed | 2024-04-14T01:38:02Z |
| format | Article |
| id | doaj.art-e0539195c4b04f6bb20071f61a847210 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T01:38:02Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e0539195c4b04f6bb20071f61a8472102022-12-22T02:19:52ZengIEEEIEEE Access2169-35362022-01-0110896348964410.1109/ACCESS.2022.32012449866059Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based RecommendationQuan Li0https://orcid.org/0000-0001-6211-1046Xinhua Xu1Jinjun Liu2Guangmin Li3Department of Computer and Information Engineering, Hubei Normal University, Huangshi, ChinaDepartment of Computer and Information Engineering, Hubei Normal University, Huangshi, ChinaDepartment of Computer and Information Engineering, Hubei Normal University, Huangshi, ChinaDepartment of Computer and Information Engineering, Hubei Normal University, Huangshi, ChinaRecent study shows that recommendation system not only relys on user’s static preference, but also dynamic preference. Consequently, it leads to the emergence of session-based recommendation. With the development of recurrent neural network, this kind of method can capture representations of users’ sequential behaviors from a large number of sessions. However it is prone to spurious dependency problem. Recently, convolutional neural network has also shown its potential in modelling session, especially in extracting complex local pattern of subsequence. Therefore, we propose a hybrid neural model, called SGPD, for learning sequential general pattern and dependency for session-based recommendation. In SGPD, we propose recurrent residual convolution network to extract general pattern of subsequence in a session. Furthermore, the SGPD scans sequence from forward and reverse direction by bidirectional recurrent neural network, and learns sequential dependency of a session. Finally, the objective function is constructed by cross entropy and the model parameters are learned. The experimental results show that the precision rate, recall rate and mean reciprocal ranking of SGPD are greatly improved compared with the state-of-art methods. It has good application prospect.https://ieeexplore.ieee.org/document/9866059/Session-based recommendationrecurrent residual convolutionbidirectional recurrent neural networkcross entropy |
| spellingShingle | Quan Li Xinhua Xu Jinjun Liu Guangmin Li Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation IEEE Access Session-based recommendation recurrent residual convolution bidirectional recurrent neural network cross entropy |
| title | Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation |
| title_full | Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation |
| title_fullStr | Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation |
| title_full_unstemmed | Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation |
| title_short | Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation |
| title_sort | learning sequential general pattern and dependency via hybrid neural model for session based recommendation |
| topic | Session-based recommendation recurrent residual convolution bidirectional recurrent neural network cross entropy |
| url | https://ieeexplore.ieee.org/document/9866059/ |
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