A Plug-and-play Model-agnostic Embedding Enhancement Approach for Explainable Recommendation

• URL: http://arxiv.org/abs/2509.03130v1
• Date: Wed, 03 Sep 2025 08:32:20 GMT
• Title: A Plug-and-play Model-agnostic Embedding Enhancement Approach for Explainable Recommendation
• Authors: Yunqi Mi, Boyang Yan, Guoshuai Zhao, Jialie Shen, Xueming Qian,
• Abstract summary: RVRec is a plug-and-play model-agnostic embedding enhancement approach.<n>It can improve both personality and explainability of existing systems.
• Score: 35.78577182946339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multimedia recommender systems provide users with suggestions of media by evaluating the similarities, such as games and movies. To enhance the semantics and explainability of embeddings, it is a consensus to apply additional information (e.g., interactions, contexts, popularity). However, without systematic consideration of representativeness and value, the utility and explainability of embedding drops drastically. Hence, we introduce RVRec, a plug-and-play model-agnostic embedding enhancement approach that can improve both personality and explainability of existing systems. Specifically, we propose a probability-based embedding optimization method that uses a contrastive loss based on negative 2-Wasserstein distance to learn to enhance the representativeness of the embeddings. In addtion, we introduce a reweighing method based on multivariate Shapley values strategy to evaluate and explore the value of interactions and embeddings. Extensive experiments on multiple backbone recommenders and real-world datasets show that RVRec can improve the personalization and explainability of existing recommenders, outperforming state-of-the-art baselines.

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