Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

Recommender Systems (RS) aim at predicting items or ratings of items that the user are
interested in. Collaborative Filtering (CF) algorithms such as user-and item-based methods …

The advent of high‐speed Internet connections has revolutionized the way research is being
carried out to obtain relevant information. Conversely, retrieving pertinent information from …

With the development of sophisticated e-learning environments, personalization is
becoming an important feature in e-learning systems due to the differences in background …

Recently, tag recommendation (TR) has become a very hot research topic in data mining
and related areas. However, neither co-occurrence based methods which only use the item …

Many researchers have introduced tag information to recommender systems to improve the
performance of traditional recommendation techniques. However, user-defined tags will …

Collaborative tagging systems allow users to assign keywords–so called “tags”–to
resources. Tags are used for navigation, finding resources and serendipitous browsing and …

Last year has witnessed the re-flourishment of tag-aware recommender systems supported
by the LLM-enriched tags. Unfortunately, though large efforts have been made, current …

Hashtags have become a powerful tool in social platforms such as Twitter to categorize and
search for content, and to spread short messages across members of the social network. In …

Social resource sharing systems are central elements of the Web 2.0 and use the same kind
of lightweight knowledge representation, called folksonomy. Their large user communities …