In collaborative filtering, distance metric learning has been applied to matrix factorization techniques with promising results. However, matrix factorization lacks the ability of capturing collaborative information, which has been remarked by recent works and improved by interpreting user interactions as signals. This paper aims to find out how metric learning connect to these signal-based models. By adopting a generalized distance metric, we discovered that in signal-based models, it is easier to estimate the residual of distances, which refers to the difference between the distances from a user to a target item and another item, rather than estimating the distances themselves. Further analysis also uncovers a link between the normalization strength of interaction signals and the novelty of recommendation, which has been overlooked by existing studies. Based on the above findings, we propose a novel model to learn a generalized distance user-item distance metric to capture user preference in interaction signals by modeling the residuals of distance. The proposed CoRML model is then further improved in training efficiency by a newly introduced approximated ranking weight. Extensive experiments conducted on 4 public datasets demonstrate the superior performance of CoRML compared to the state-of-the-art baselines in collaborative filtering, along with high efficiency and the ability of providing novelty-promoted recommendations, shedding new light on the study of metric learning-based recommender systems.

翻译：在协同过滤中，距离度量学习已被应用于矩阵分解技术并取得了显著成果。然而，矩阵分解缺乏捕捉协同信息的能力——这一缺陷已被近期研究指出，并通过将用户交互解释为信号而得到改进。本文旨在探究度量学习如何与这些基于信号的模型建立联系。通过采用广义距离度量，我们发现基于信号的模型更容易估计距离的残差（即用户到目标物品的距离与用户到另一物品的距离之差），而非直接估计距离本身。进一步分析还揭示了交互信号归一化强度与推荐新颖性之间被现有研究忽视的关联。基于上述发现，我们提出了一种新型模型，通过建模距离残差来学习广义用户-物品距离度量，从而捕捉交互信号中的用户偏好。所提出的CoRML模型进一步通过新引入的近似排序权重提升了训练效率。在四个公开数据集上的大量实验表明，CoRML在协同过滤中优于最先进的基线模型，兼具高效率与提供新颖性提升推荐的能力，为基于度量学习的推荐系统研究提供了新思路。