Attentive Aspect Modeling for Review-aware Recommendation
This work addresses sparsity and static interest issues in review-aware recommendation, offering an incremental improvement for e-commerce and content platforms.
The paper tackled the sparsity of common aspects and static user interest assumptions in aspect-based recommendation by proposing AARM, which models synonymous and similar aspects and uses neural attention to capture dynamic user interest, achieving significant performance improvements over state-of-the-art methods on top-N recommendation.
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.