In recent years, a variety of review-based recommender systems have been developed, with
the goal of incorporating the valuable information in user-generated textual reviews into the …

In recent times, the recommender systems (RSs) have considerable importance in
academia, commercial activities, and industry. They are widely used in various domains …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Collaborative filtering (CF) is a common recommendation approach that relies on user-item
ratings. However, the natural sparsity of user-item rating data can be problematic in many …

We describe a novel, multi-task recommendation model, which jointly learns to perform
rating prediction and recommendation explanation by combining matrix factorization, for …

Abstract The use of Online Social Networks has been rapidly increased over the last years.
In particular, Social Media Networks allow people to communicate, share, comment and …

Social media allow web users to create and share content pertaining to different subjects,
exposing their activities, opinions, feelings and thoughts. In this context, online social media …

Recommender Systems are more and more playing an important role in our life,
representing useful tools helping users to find “what they need” from a very large number of …

The explanation interface has been recognized important in recommender systems as it can
help users evaluate recommendations in a more informed way for deciding which ones are …

In the world of recommender systems, so-called content-based methods are an important
approach that rely on the availability of detailed product or item descriptions to drive the …