Multi-level attentive deep user-item representation learning for recommendation system
With the development of e-commerce platforms, user reviews have become a vital source of information to address the sparsity problems for enhancing the predictive performance of the recommendation systems (RSs). However, the traditional methods of the RSs used to model user/item latent features base...
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| Main Authors: | , , |
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| 格式: | Article |
| 出版: |
Elsevier B.V.
2021
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/95815/ http://dx.doi.org/10.1016/j.neucom.2020.12.043 |
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| 機構: | Universiti Teknologi Malaysia |
| 總結: | With the development of e-commerce platforms, user reviews have become a vital source of information to address the sparsity problems for enhancing the predictive performance of the recommendation systems (RSs). However, the traditional methods of the RSs used to model user/item latent features based on static vectors in an independent manner without considering the dynamic nature of the user-item interactions which potentially affect the accuracy of the recommendation process. Thus, this paper proposes a RS model that exploits neural attention techniques to learn user/item representations by jointly considering the fine-grained semantic information for the user-item pairs. The proposed model utilizes both review-based and interaction-specific features for the user/item reviews to learn heterogeneous user/item representations. First, a BiLSTM sequence encoder is used to learn the contextual information of words, and a Co-attention network is then designed to jointly capture the most relevant semantic information of reviews for the user-item pair. To better capture user/item latent factors comprehensively, interaction-specific features based on the rating scores are further integrated with the review-specific latent features via a shared hidden layer. Finally, an attentive factorization machine (FM) is then applied on the shared hidden layer of the integrated user/item features for the final prediction. We carry out a series of experiments using real-world datasets and the results demonstrate that our proposed method is better than the baseline approaches in terms of both rating prediction and ranking performance. |
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