Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …

Recommender Systems are currently highly relevant for helping users deal with the
information overload they suffer from the large volume of data on the web, and automatically …

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 …

Recommendation accuracy is a fundamental problem in the quality of the recommendation
system. In this article, we propose an efficient deep matrix factorization (EDMF) with review …

Heterogeneous information network (HIN) has been widely adopted in recommender
systems due to its excellence in modeling complex context information. Although existing …

With the revival of neural networks, many studies try to adapt powerful sequential neural
models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based …

Data science has employed great research efforts in developing advanced analytics,
improving data models and cultivating new algorithms. However, not many authors have …

Click-through rate (CTR) prediction has been one of the most central problems in
computational advertising. Lately, embedding techniques that produce low-dimensional …

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous
information exchange and content sharing among vehicles with little or no human …

The interactions of users and items in recommender system could be naturally modeled as a
user-item bipartite graph. In recent years, we have witnessed an emerging research effort in …