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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 comparedwith 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 fromthe perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorizationbased 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. [ABSTRACT FROM AUTHOR]

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field. [ABSTRACT FROM AUTHOR]