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

Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

Several recent works have considered the problem of generating reviews (or 'tips') as a form
of explanation as to why a recommendation might match a customer's interests. While …

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

Reviews information is dominant for users to make online purchasing decisions in e-
commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews …

With the rapid development of electronic commerce, the availability of a large amount of
information on the products, as well as from other users, make the customers' decision …

Many recent state-of-the-art recommender systems such as D-ATT, TransNet and
DeepCoNN exploit reviews for representation learning. This paper proposes a new neural …

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

Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

We propose a novel deep learning hybrid recommender system to address the gaps in
Collaborative Filtering systems and achieve the state-of-the-art predictive accuracy using …