Preference Logical Reasoning with Preference Operators for Explainable Recommendations

Fei Li, Enneng Yang, Guibing Guo, Linying Jiang, Jianzhe Zhao, Xingwei Wang

Abstract: Preference logical reasoning utilizes user-item interactions (e.g., ratings and reviews) to infer user preferences and discover user decision paths from the knowledge graph to enhance the explainability of item recommendations. However, existing algorithms assume that the ratings and reviews of any item are always consistent, ignoring situations where items with high ratings have negative reviews or items with low ratings but positive reviews. This leads to inaccurate learning of user preferences. In fact, through experimental analysis of two real datasets, we found that on average, about 10% of the interactive data exhibited this inconsistency, that is, items with high ratings but negative reviews appear in the recommendation list. To address this issue, we propose a general preference logical reasoning method based on preference operators. Specifically, we capture the semantic information of users toward the item (its corresponding attributes) in reviews and define two preference operators (like and dislike) for the item to correct ambiguous neutral ratings or false ratings that do not reflect true preferences. In the process of preference path reasoning, the like preference operator increases the occurrence probability of liked items, while the dislike preference operator reduces the occurrence probability of disliked items. By fusing the preference operators in the preference path, we obtain consistent user preferences and enhance the explainability of item recommendations. The experimental results on four real datasets demonstrate that our method can effectively improve the performance of all comparison baselines in terms of recommendation accuracy and user decision explainability.

External IDs:dblp:journals/tois/LiYGJZW25