Attentive preference personalized recommendation with sentence-level explanations

Personalized recommendation mostly employs users' historical data to improve their user profiles, and these profiles are then used as the bases for recommendations. Because reviews can contain a large amount of information regarding user preferences and item features, they can be naturally into recommender systems (RSs) as contextual information, thus solving the problem of data sparsity and helping to provide personalized recommendations. The existing technology mainly extracts latent representations of users or items in an independent and static manner. We argue that static embedding cannot fully capture a user's preferences. Indeed, a user will have different preferences corresponding to different items. This type of review-based recommendation model cannot provide a personalized, and complete semantic explanation of a candidate recommendation item to a user. In this paper, we introduce an attention mechanism to explore the importance of specific sentences in reviews for different users and propose a novel attentive preference personalized recommendation with sentence-level explanations (APSE). The APSE employs the latent features of users and items and the latent factors of their pairwise interactions to obtain review representations. Then, the APSE uses probability matrix factorization to model additional high-level feature interactions based on these user-item pairs for rating prediction. We implement review feature learning in the APSE to exploit review data in which an attentive mechanism is used to highlight the influences of words and sentences to achieve focused paragraph embedding. Finally, the APSE also employs an explanation sentence judgment mechanism that implements the user item pair interaction method to extract comments or statements that pertain to user preferences as recommendation interpretations. Experiments are performed on real-world datasets for validation. Additionally, we show the important words and sentences highlighted by the attentive mechanism. At the end of the experiment, a specific item explanation for a user is produced and compared with the user's existing comments. The results show that the performance of the APSE can exceed that of various recommended models when the available ratings are limited. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Personalized recommendation relies on user historical data and reviews to enhance user profiles and address data sparsity, but current static embedding methods may not fully capture user preferences and provide complete semantic explanations for recommendations.