LAUSR

Convolutional neural network (CNN)-based recommender systems are playing an increasingly significant role in the vigorous development of Industrial Internet of Things, and have made great contributions to analyzing and mining a large amount of data to provide various services for terminal users. However, as the lack of explainability in deep learning, users often have low trust in the system due to their incomprehension of recommendation results. In addition, recommender systems have been facing a serious sparsity problem, and relying only on sparse rating data to learn user preferences and similarities may face malicious recommendation attacks. The abovementioned problems have been hindering the further improvement of recommendation performance. Therefore, in order to effectively alleviate the sparsity problem and meanwhile enhance the trustworthiness, an auxiliary review-based personalized attentional CNN (ARPCNN) is proposed in this article. By applying the proposed personalized word-level attention mechanism and personalized review-level attention mechanism in parallel CNNs, critical words and informative reviews are given high attention weights. Moreover, a user auxiliary network is proposed, which regards the reviews written by kindred spirits who have a trust relationship with the user as auxiliary reviews, and effectively extracts the user’s auxiliary review features, thereby achieving more accurate user modeling to improve the recommendation performance. Extensive experiments are conducted on four real-world datasets, and the results show that the performance of the proposed model is better than that of baselines, which verifies the effectiveness of ARPCNN.