Recommendation systems are gaining increasing popularity in many application areas like
e-commerce, movie and music recommendations, tourism, news, advertisement, stock …

Recommender systems have recently been singled out as a fascinating area of research,
owing to the technological progress in mobile devices, such as smartphones and tablets, as …

We present recursive recurrent neural networks with attention modeling (R2AM) for lexicon-
free optical character recognition in natural scene images. The primary advantages of the …

Although latent factor models (eg, matrix factorization) achieve good accuracy in rating
prediction, they suffer from several problems including cold-start, non-transparency, and …

Personalized rating prediction is an important research problem in recommender systems.
Although the latent factor model (eg, matrix factorization) achieves good accuracy in rating …

User-based collaborative filtering, a widely used nearest neighbour-based recommendation
technique, predicts an item's rating by aggregating its ratings from similar users. User …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Intelligent recommendation technology has been playing an increasingly important role in
various industry applications such as e-commerce product promotion and Internet …

With the rapid development of Internet movie industry social-aware movie recommendation
systems (SMRs) have become a popular online web service that provide relevant movie …

Scientific article recommender systems are playing an increasingly important role for
researchers in retrieving scientific articles of interest in the coming era of big scholarly data …