Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

摘要深度学习是机器学习领域一个重要研究方向, 近年来在图像处理, 自然语言理解,
语音识别和在线广告等领域取得了突破性进展. 将深度学习融入推荐系统中 …

Learning vector representations (aka. embeddings) of users and items lies at the core of
modern recommender systems. Ranging from early matrix factorization to recently emerged …

Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …

Reorganizing implicit feedback of users as a user-item interaction graph facilitates the
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …

This paper proposes a recommender system to alleviate the cold-start problem that can
estimate user preferences based on only a small number of items. To identify a user's …

User behavior data in recommender systems are driven by the complex interactions of many
latent factors behind the users' decision making processes. The factors are highly entangled …

Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …