AOTree: Aspect Order Tree-based Model for Explainable Recommendation

TL;DR

AOTree is a novel explainable recommendation model that incorporates aspect order dependencies inspired by cognitive psychology, improving prediction accuracy and interpretability by modeling the sequence of decision factors.

Contribution

The paper introduces AOTree, the first model to integrate aspect order relationships into explainable recommendations, enhancing both performance and explanation clarity.

Findings

AOTree outperforms baseline models in rating prediction accuracy.

The model's explanations align better with user decision processes.

Experimental results confirm the effectiveness of aspect order modeling.

Abstract

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.