提升推荐系统嵌入表示的个性化与可解释性，无需修改原有模型。
A Plug-and-play Model-agnostic Embedding Enhancement Approach for Explainable Recommendation
- 基于负2-Wasserstein距离的对比损失优化嵌入表示
- 用多变量Shapley值重加权交互信息，评估其贡献价值
- 即插即用，适配各类推荐模型，提升可解释性
现有多媒体推荐系统通过评估相似性（如游戏和电影）为用户提供建议。为增强嵌入的语义与可解释性，通常引入额外信息（如用户行为、上下文、流行度）。然而，若不系统考虑表示能力与价值，嵌入的实用性和可解释性会大幅下降。为此，我们提出RVRec——一种即插即用、模型无关的嵌入增强方法，可同时提升推荐系统的个性化与可解释性。具体地，我们设计了一种基于概率的嵌入优化方法，采用基于负2-Wasserstein距离的对比损失，以增强嵌入的代表性；同时引入基于多变量Shapley值的重加权策略，评估并挖掘交互与嵌入的价值。在多个基线推荐器与真实数据集上的大量实验表明，RVRec能有效提升现有推荐系统的个性化与可解释性，优于当前最优基线。
原文摘要 · Abstract (English)
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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