Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation
This work addresses evaluation inconsistencies for researchers and practitioners in knowledge-aware recommendation systems, but it is incremental as it focuses on replication and comparative analysis rather than introducing new methods.
The paper tackled the problem of heterogeneous and limited evaluation protocols in path reasoning recommendation methods by replicating three state-of-the-art approaches under a common protocol. It studied their performance on recommendation utility, explanation quality, and fairness, providing insights into progress and open issues in the field.
Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths between recommended products and already experienced products and, then, turn such paths into textual explanations for the user. Unfortunately, evaluation protocols in this field appear heterogeneous and limited, making it hard to contextualize the impact of the existing methods. In this paper, we replicated three state-of-the-art relevant path reasoning recommendation methods proposed in top-tier conferences. Under a common evaluation protocol, based on two public data sets and in comparison with other knowledge-aware methods, we then studied the extent to which they meet recommendation utility and beyond objectives, explanation quality, and consumer and provider fairness. Our study provides a picture of the progress in this field, highlighting open issues and future directions. Source code: \url{https://github.com/giacoballoccu/rep-path-reasoning-recsys}.