Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users’ feedback are usually heterogeneous (rather than homogeneous) such as purchases and examinations in e-commerce, which reflects users’ preferences in different degrees. Effective modeling of such heterogeneous one-class feedback is challenging compared with that of homogeneous feedback of ratings. As a response, heterogeneous one-class collaborative filtering (HOCCF) is proposed, which often converts the heterogeneous feedback into two parts (i.e., target feedback and auxiliary feedback), aiming to care more about the target feedback (e.g., purchases ) with the assistance of the auxiliary feedback (e.g., examinations ). In this survey, we provide an overview of the representative HOCCF methods from the perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorization-based methods according to different strategies. Second, we describe the transfer learning-based methods with different knowledge sharing manners. Third, we discuss the deep learning-based methods according to the neural architectures. Moreover, we include some important example applications, describe the empirical studies, and discuss some promising future directions.

中文翻译：

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异构一类协同过滤综述

在信息过载的情况下，推荐系统在为用户提供个性化服务方面发挥着重要作用。通常，用户对项目的反馈通常包含反映他们偏好的最重要的信息，这可以实现准确的个性化推荐。在实际应用中，用户的反馈通常是异质的（而不是同质的），例如购买和考试在电子商务中，不同程度地反映了用户的偏好。与评级的同质反馈相比，这种异构的一类反馈的有效建模具有挑战性。作为回应，提出了异构一类协同过滤（HOCCF），它通常将异构反馈转化为两部分（即目标反馈和辅助反馈），旨在更关心目标反馈（例如，购买）在辅助反馈的帮助下（例如，考试）。在本次调查中，我们从基于分解的方法、基于迁移学习的方法和基于深度学习的方法的角度概述了具有代表性的 HOCCF 方法。首先，我们根据不同的策略回顾了基于分解的方法。其次，我们描述了具有不同知识共享方式的基于迁移学习的方法。第三，我们根据神经架构讨论基于深度学习的方法。此外，我们包括一些重要的示例应用程序，描述了实证研究，并讨论了一些有前途的未来方向。