An Attentive Aspect-Based Recommendation Model With Deep Neural Network

Abstract

With the growth of the internet and e-commerce, online reviews have become a prevalent and rich source of information for personalized recommendations. Review text typically contains user preferences regarding various aspects of items or services. However, most previous recommendation models that used online reviews focused on general opinions, without considering the various aspects of the review text. Although previous approaches have effectively reflected the overall preferences embedded in reviews, capturing the preferences for various aspects is limited. This paper proposes an attentive aspect-based recommendation model with a deep neural network (AARN), which can capture user preferences regarding various aspects embedded in review text. The proposed model uses an advanced aspect-based sentiment analysis (ABSA) method. Unlike previous approaches that extract overall preferences, ABSA offers the advantage of extracting preferences for various aspects embedded in review texts. An attention mechanism is then applied to measure the unique attention weights for each aspect. Extensive experiments were conducted on real-world online review datasets, showing that the proposed model outperformed baseline models in effectively predicting user ratings for target items. Furthermore, this study demonstrated the influence of the ABSA method on personalized and explainable recommendations.

Keywords

• Aspect-based sentiment analysis
• attention mechanism
• deep learning
• online review
• recommendation model
• user preference