Graph-based Extractive Explainer for Recommendations
This work addresses the problem of improving explanation quality in recommender systems for users, though it appears incremental as it builds on existing extractive and graph-based methods.
The paper tackles the challenge of generating easily perceivable, reliable, and personalized explanations in recommender systems by developing a graph attentive neural network model that integrates user, item, attributes, and sentences for extractive explanations, with empirical evaluations on two benchmark datasets demonstrating its generation quality.
Explanations in a recommender system assist users in making informed decisions among a set of recommended items. Great research attention has been devoted to generating natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable and personalized at the same time. In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes, and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage, and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of the proposed solution.