Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations, these methods however implicitly deprioritize the modeling of user-wise similarity in the embedding space; consequently, identifying similar users is underperforming, and additional processing schemes are usually required otherwise. To avoid thorough model re-training, we propose WSFE, a model-agnostic and training-free representation encoder, to be flexibly employed on the fly for effective user segmentation. Underpinned by the optimal transport theory, the encoded representations from WSFE present a matched user-wise similarity/distance measurement between the realistic and embedding space. We incorporate WSFE into six state-of-the-art recommender models and conduct extensive experiments on six real-world datasets. The empirical analyses well demonstrate the superiority and generality of WSFE to fuel multiple downstream tasks with diverse underlying targets in recommendation.

翻译：基于向量化嵌入最大化用户-物品交互是近期推荐模型的标准化流程。尽管这些方法在物品推荐方面表现出色，但它们隐性地降低了嵌入空间中对用户间相似性建模的优先级；因此，识别相似用户的效果欠佳，通常需要额外的处理方案。为避免彻底的模型重新训练，我们提出WSFE——一种模型无关且无需训练的表征编码器，可灵活即时部署以实现高效的用户分割。基于最优传输理论，WSFE生成的编码表征在真实空间与嵌入空间之间呈现匹配的用户间相似性/距离度量。我们将WSFE集成到六种最先进的推荐模型中，并在六个真实数据集上进行了广泛实验。实证分析充分证明了WSFE在促进推荐系统中具有多样化底层目标的多项下游任务方面的优越性与通用性。