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

Recently, deep neural networks have been widely applied to recommender systems. A
representative work is to utilize deep learning for modeling complex user-item interactions …

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

With the boosting of neural networks, recommendation methods become significantly
improved by their powerful ability of prediction and inference. Existing neural-network based …

Recommender systems are aimed at generating a personalized ranked list of items that an
end user might be interested in. With the unprecedented success of deep learning in …

Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction information …

In this paper we investigate the effectiveness of Recurrent Neural Networks (RNNs) in a top-
N content-based recommendation scenario. Specifically, we propose a deep architecture …

Existing benchmark datasets for recommender systems (RS) either are created at a small
scale or involve very limited forms of user feedback. RS models evaluated on such datasets …

Research on recommender systems algorithms, like other areas of applied machine
learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of …

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 …