SMAR: Summary-Aware Multi-Aspect Recommendation

TL;DR: This paper proposes SMAR, a recommendation system that leverages user summaries to select key reviews, capture fine-grained user preferences, and model item aspects, outperforming state-of-art approaches in rating prediction accuracy on Amazon datasets.

Abstract: Extracting user preferences and item features from reviews to assist recommendations is becoming increasingly popular. However, on the one hand, existing works generally select reviews based on how well user reviews match item reviews. They ignore that reviews may contain noise such as irrelevant phrases, which will affect the accuracy of selecting important reviews. In contrast, summaries written by users are abstracts of reviews that contain critical item feature information. They can be adopted to identify crucial reviews and further capture user’s fine-grained preferences from reviews. In addition, current methods do not consider that different items have different aspects in the same domain. They normally set a fixed number of aspects of the entire domain to get coarse-grained user preferences and item features. However, when modeling the user’s preferences for the current item, it might be more important to capture the corresponding aspects of the item preferences. Therefore, in this paper, we are motivated to propose a Summary-Aware Multi-Aspect Recommendation (SMAR). Specifically, we first construct a Summary-Aware Review Selection Module which adopts summaries to alleviate noise in reviews, identifying key reviews accurately. We then design a Summary-Aware Multi-Aspect Module which captures targeted user preferences towards the current item’s aspects. Finally, we employ Latent Factor Model to complete the recommendation process. The experimental results on Amazon datasets show that our method significantly outperfoms state-of-art approaches in terms of rating prediction accuracy.