Recommender systems use algorithms to provide users with product or service
recommendations. Recently, these systems have been using machine learning algorithms …

Purpose This paper aims to identify, evaluate and integrate the findings of all relevant and
high-quality individual studies addressing one or more research questions about …

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 …

Recommender systems have been regarded as gaining a more significant role with the
emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF …

Trust-aware recommender systems are advanced approaches which have been developed
based on social information to provide relevant suggestions to users. These systems can …

An efficient and accurate recommender system provides online users with a variety of
personalized recommendation services, thus effectively improving the satisfaction and …

Abstract We propose “Deep Autoencoders for Feature Learning in Recommender Systems,”
a novel discriminative model based on the incorporation of features from autoencoders in …

To the best of our knowledge, few studies have focused on the inconsistency between user
ratings and reviews as well as natural noise management in recommender systems (RSs) …

Abstract The Recommender System (RS) plays an important role in information retrieval
techniques in a bid to handle massive online data effectively. It gives suggestions on …

Recommender system (RS) is an emerging technique in information retrieval to handle a
large amount of online data effectively. It provides recommendation to the online user in …