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.
Federated Semantic Residuals for Multimodal Recommendation
Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息学院澳大利亚人工智能研究所）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
Collaborative residual learners for automatic icd10 prediction using prescribed medications
Growing Data；BioMedical Informatics center, Faculty of Medicine, Chiang Mai University（清迈大学医学院生物医学信息中心）；Faculty of Medicine, Chiang Mai University（清迈大学医学院）；Faculty of Engineering, Chiang Mai University（清迈大学工程学院）；Biomedical Engineering Institute, Chiang Mai University（清迈大学生物医学工程研究所）；Maharaj Nakhon Chiang Mai hospital（玛哈拉吉那空清迈医院）；IT department of Maharaj Nakhon Chiang Mai hospital（玛哈拉吉那空清迈医院信息技术部）
Carnegie Mellon University（卡内基梅隆大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Columbia University（哥伦比亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）；Rice University（莱斯大学）
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
Faculty of Science and Technology, Charles Darwin University（查尔斯达尔文大学科学技术学院）；University of Electronic Science and Technology of China（电子科技大学）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Institute of Information Engineering, CAS（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Alibaba Group（阿里巴巴集团）；Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）
协作度量学习（CML）最近成为推荐系统（RS）中的一种流行方法，缩小了度量学习和协作过滤之间的差距。遵循RS的惯例，现有的方法在其模型设计中利用唯一的用户表示。本文重点介绍了一个具有挑战性的情况下，用户有多个类别的利益。在这种情况下，我们认为，独特的用户表示可能会导致偏好偏差，特别是当项目类别分布不平衡。为了解决这个问题，我们提出了一种新的方法称为\textit{Diversity-Promoting Collaborative Metric Learning}（DPCML），希望考虑到通常被忽视的少数用户的利益。DPCML背后的关键思想是为系统中的每个用户包括多个表示集。基于此嵌入范式，用户对一个项目的偏好聚合从不同的嵌入之间的最小项目用户的距离用户嵌入集。此外，我们观察到，同一用户的嵌入的多样性在模型中也起着至关重要的作用。为此，我们提出了一个\textit{diversity control regularization}术语，以更好地适应多向量表示策略。从理论上讲，我们表明，DPCML可以很好地推广到看不见的测试数据，通过解决来自最小值的恼人的操作的挑战。在一系列基准数据集上的实验证明了DPCML的有效性。
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Peng Cheng Laboratory（鹏城实验室）
Improved Diversity-Promoting Collaborative Metric Learning for Recommendation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Alibaba Group（阿里巴巴集团）；Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）
