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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517473/ https://www.ncbi.nlm.nih.gov/pubmed/33286641 http://dx.doi.org/10.3390/e22080870 |
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author | Zhao, Chuanchuan You, Jinguo Wen, Xinxian Li, Xiaowu |
author_facet | Zhao, Chuanchuan You, Jinguo Wen, Xinxian Li, Xiaowu |
author_sort | Zhao, Chuanchuan |
collection | PubMed |
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). |
format | Online Article Text |
id | pubmed-7517473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75174732020-11-09 Deep Bi-LSTM Networks for Sequential Recommendation Zhao, Chuanchuan You, Jinguo Wen, Xinxian Li, Xiaowu Entropy (Basel) Article 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). MDPI 2020-08-07 /pmc/articles/PMC7517473/ /pubmed/33286641 http://dx.doi.org/10.3390/e22080870 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhao, Chuanchuan You, Jinguo Wen, Xinxian Li, Xiaowu Deep Bi-LSTM Networks for Sequential Recommendation |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517473/ https://www.ncbi.nlm.nih.gov/pubmed/33286641 http://dx.doi.org/10.3390/e22080870 |
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