Deep Bi-LSTM Networks for Sequential Recommendation
Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature inform...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/8/870 |
| _version_ | 1797559606623338496 |
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| author | Chuanchuan Zhao Jinguo You Xinxian Wen Xiaowu Li |
| author_facet | Chuanchuan Zhao Jinguo You Xinxian Wen Xiaowu Li |
| author_sort | Chuanchuan Zhao |
| collection | DOAJ |
| description | Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the issues in a three-pronged attack: the improved item embedding, weight update, and the deep bidirectional LSTM preference learning. First, the user-item sequences are embedded into a low-dimensional item vector space representation via Item2vec, and the class label vectors are concatenated for each embedded item vector. Second, the embedded item vectors learn different impact weights of each item to achieve item awareness via self-attention mechanism; the embedded item vectors and corresponding weights are then fed into the bidirectional LSTM model to learn the user preference vectors. Finally, the top similar items in the preference vector space are evaluated to generate the recommendation list for users. By conducting comprehensive experiments, we demonstrate that our model outperforms the traditional recommendation algorithms on Recall@20 and Mean Reciprocal Rank (MRR@20). |
| first_indexed | 2024-03-10T17:48:43Z |
| format | Article |
| id | doaj.art-399a924ddaff460fa228be653e4d3d54 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T17:48:43Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-399a924ddaff460fa228be653e4d3d542023-11-20T09:26:47ZengMDPI AGEntropy1099-43002020-08-0122887010.3390/e22080870Deep Bi-LSTM Networks for Sequential RecommendationChuanchuan Zhao0Jinguo You1Xinxian Wen2Xiaowu Li3Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaRecent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the issues in a three-pronged attack: the improved item embedding, weight update, and the deep bidirectional LSTM preference learning. First, the user-item sequences are embedded into a low-dimensional item vector space representation via Item2vec, and the class label vectors are concatenated for each embedded item vector. Second, the embedded item vectors learn different impact weights of each item to achieve item awareness via self-attention mechanism; the embedded item vectors and corresponding weights are then fed into the bidirectional LSTM model to learn the user preference vectors. Finally, the top similar items in the preference vector space are evaluated to generate the recommendation list for users. By conducting comprehensive experiments, we demonstrate that our model outperforms the traditional recommendation algorithms on Recall@20 and Mean Reciprocal Rank (MRR@20).https://www.mdpi.com/1099-4300/22/8/870recommendation systemsinteractive sequenceclass labeldeep bidirectional LSTMself-attentionitem similarity |
| spellingShingle | Chuanchuan Zhao Jinguo You Xinxian Wen Xiaowu Li Deep Bi-LSTM Networks for Sequential Recommendation Entropy recommendation systems interactive sequence class label deep bidirectional LSTM self-attention item similarity |
| title | Deep Bi-LSTM Networks for Sequential Recommendation |
| title_full | Deep Bi-LSTM Networks for Sequential Recommendation |
| title_fullStr | Deep Bi-LSTM Networks for Sequential Recommendation |
| title_full_unstemmed | Deep Bi-LSTM Networks for Sequential Recommendation |
| title_short | Deep Bi-LSTM Networks for Sequential Recommendation |
| title_sort | deep bi lstm networks for sequential recommendation |
| topic | recommendation systems interactive sequence class label deep bidirectional LSTM self-attention item similarity |
| url | https://www.mdpi.com/1099-4300/22/8/870 |
| work_keys_str_mv | AT chuanchuanzhao deepbilstmnetworksforsequentialrecommendation AT jinguoyou deepbilstmnetworksforsequentialrecommendation AT xinxianwen deepbilstmnetworksforsequentialrecommendation AT xiaowuli deepbilstmnetworksforsequentialrecommendation |