Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 33, Issue 11, Pages 6726-6736Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3083264
Keywords
Predictive models; Semantics; Recommender systems; Collaboration; Backpropagation; Neural networks; Electronic mail; Deep autoencoder; deep neural networks (DNNs); ratings and reviews; recommender systems
Categories
Funding
- NSFC [61876193]
- Natural Science Foundation of Guangdong Province [2020A1515110337]
- Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University [2020B1212060032]
- Open Foundation of Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application
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Many recommender systems utilize review text as auxiliary information to enhance recommendation quality, but existing models typically use ratings as the ground truth for error backpropagation, potentially resulting in the loss of valuable review information. This article introduces a novel deep model DRRNN, which considers both target ratings and reviews as ground truth for error backpropagation, allowing for the retention of more semantic information in rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.
To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error backpropagation. However, the rating information can only indicate the users' overall preference for the items, while the review text contains rich information about the users' preferences and the attributes of the items. In real life, reviews with the same rating may have completely opposite semantic information. If only the ratings are used for error backpropagation, the latent factors of these reviews will tend to be consistent, resulting in the loss of a large amount of review information. In this article, we propose a novel deep model termed deep rating and review neural network (DRRNN) for recommendation. Specifically, compared with the existing models that adopt the review text as the auxiliary information, DRRNN additionally considers both the target rating and target review of the given user-item pair as ground truth for error backpropagation in the training stage. Therefore, we can keep more semantic information of the reviews while making rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.
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