Faithfully Explainable Recommendation via Neural Logic Reasoning
This work addresses the need for trustworthy and interpretable AI in e-commerce recommendations, though it is incremental as it builds on existing KG reasoning frameworks by focusing on faithfulness.
The paper tackles the problem of generating faithful explanations for knowledge graph-based recommender systems by proposing LOGER, a neural logic reasoning method that uses interpretable logical rules to guide path reasoning, and demonstrates its effectiveness on three large-scale e-commerce datasets with high-quality recommendations and faithful explanations.
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.