WSFE：用于协同过滤中有效用户分段的Wasserstein子图特征编码器
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
AI总结：
针对推荐模型中用户相似性建模不足的问题，提出模型无关、免训练的WSFE编码器，基于最优传输理论实现有效用户分段，并在六个模型和数据集上验证其优越性与通用性。
摘要：
基于向量化嵌入最大化用户-物品互动是近期推荐模型的标准流程。尽管这些方法在物品推荐上表现优异，但它们隐含地降低了嵌入空间中用户间相似性建模的优先级；因此，识别相似用户表现不佳，通常需要额外的处理方案。为避免彻底的模型重训练，我们提出WSFE，一种模型无关且无需训练的表征编码器，可灵活地即时用于有效的用户分段。在最优传输理论的支持下，WSFE编码的表征在现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整合到六个最先进的推荐模型中，并在六个真实世界数据集上进行广泛实验。实证分析充分证明了WSFE在推荐中为具有多样底层目标的多项下游任务提供支持的优越性和通用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
