PMAR: Multi-aspect Recommendation Based on Psychological Gap

Review-based recommendations mainly explore reviews that provide actual attributes of items for recommendation. In fact, besides user reviews, merchants have their descriptions of the items. The inconsistency between the descriptions and the actual attributes of items will bring users psychological gap caused by the Expectation Effect. Compared with the recommendation without merchant's description, users may feel more unsatisfied with the items (below expectation) or be more impulsive to produce unreasonable consuming (above expectation), both of which may lead to inaccurate recommendation results. In addition, as users attach distinct degrees of importance to different aspects of the item, the personalized psychological gap also needs to be considered. In this work, we are motivated to propose a novel Multi-Aspect recommendation based on Psychological Gap (PMAR) by modelling both user's overall and personalized psychological gaps. Specifically, we first design a gap logit unit for learning the user's overall psychological gap towards items derived from textual review and merchant's description. We then integrate a user-item co-attention mechanism to calculate the user's personalized psychological gap. Finally, we adopt Latent Factor Model to accomplish the recommendation task. The experimental results demonstrate that our model significantly outperforms the related approaches w.r.t. rating prediction accuracy on Amazon datasets.