Attentive Aspect Modeling for Review-aware Recommendation

Abstract

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.

arXiv commentCamera-ready manuscript for TOIS

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• Try This Instead: Personalized and Interpretable Substitute Recommendation
• Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
• Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
• Dual Preference Distribution Learning for Item Recommendation
• An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation
• printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
• Rating and aspect-based opinion graph embeddings for explainable recommendations
• Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction
• TADO: Time-varying Attention with Dual-Optimizer Model
• Leveraging Review Properties for Effective Recommendation
• Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations