Abstract: Many shopping websites have a large amount of review information written by users. Although most recommendation systems use review information, there is still much room for improvement. On the one hand, the information in the comments is uneven, mixed with a lot of useless information; on the other hand, most existing recommendation systems assume that a user’s attention to a certain product feature is the same for all products and cannot accurately reflect user preferences. This paper proposes an aspect-aware depth recommendation model ANAP that integrates triple attention and review score. Starting from the two levels of words and features, the important information in the review text is extracted by constructing two different attention networks to reduce the impact of useless information; in order to accurately reflect user preferences, the attention interaction network is constructed to capture the user’s different attention to various aspects of different items, and to achieve fine-grained modeling of aspect perception. This paper conducts experiments on 6 real data sets and designs an attention mechanism comparison experiment. The results show that the ANAP model effectively improves the score prediction accuracy, and the mean absolute error (MAE) is lower than the existing best algorithm by 4.86 percentage points.

Key words: attention mechanism, convolutional neural network, aspect perception, recommendation system