On the Internet, where the number of choices is overwhelming, there is need to filter,
prioritize and efficiently deliver relevant information in order to alleviate the problem of …

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

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

Advances in artificial intelligence, sensors and big data management have far-reaching
societal impacts. As these systems augment our everyday lives, it becomes increasing-ly …

As one of the most successful approaches to building recommender systems, collaborative
filtering (CF) uses the known preferences of a group of users to make recommendations or …

In this work we present topic diversification, a novel method designed to balance and
diversify personalized recommendation lists in order to reflect the user's complete spectrum …

Recommendation systems exist to help users discover content in a large body of items. An
ideal recommendation system should mimic the actions of a trusted friend or expert …

Collaborative filtering is regarded as one of the most promising recommendation algorithms.
The item-based approaches for collaborative filtering identify the similarity between two …

Recommender systems are widely used in online e-commerce applications to improve user
engagement and then to increase revenue. A key challenge for recommender systems is …

The Web has moved, slowly but steadily, from a collection of documents towards a collection
of structured data. Knowledge graphs have then emerged as a way of representing the …