AOTree: Aspect Order Tree-based Model for Explainable Recommendation
This work addresses the need for more interpretable recommender systems by capturing aspect orders, offering a domain-specific improvement for users seeking understandable recommendations.
The paper tackles the problem of explainable recommendation by incorporating ordering relationships among aspects in reviews, which existing methods ignore, and demonstrates that the proposed AOTree method improves rating predictions and aligns better with user decision-making processes.
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as words or aspects, and ignore the ordering relationship among them. This oversight neglects crucial ordering dimensions in the human decision-making process, leading to suboptimal performance. Therefore, in this paper, we propose Aspect Order Tree-based (AOTree) explainable recommendation method, inspired by the Order Effects Theory from cognitive and decision psychology, in order to capture the dependency relationships among decisive factors. We first validate the theory in the recommendation scenario by analyzing the reviews of the users. Then, according to the theory, the proposed AOTree expands the construction of the decision tree to capture aspect orders in users' decision-making processes, and use attention mechanisms to make predictions based on the aspect orders. Extensive experiments demonstrate our method's effectiveness on rating predictions, and our approach aligns more consistently with the user' s decision-making process by displaying explanations in a particular order, thereby enhancing interpretability.