Recommender systems have the effect of guiding users in a personalized way to interesting
objects in a large space of possible options. Content-based recommendation systems try to …

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 …

Content-based recommender systems (CBRSs) rely on item and user descriptions (content)
to build item representations and user profiles that can be effectively exploited to suggest …

Linked Open Data has been recognized as a valuable source for background information in
many data mining and information retrieval tasks. However, most of the existing tools require …

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 …

Recommender systems are filters which suggest items or information that might be
interesting to users. These systems analyze the past behavior of a user, build her profile that …

The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open
knowledge bases freely accessible on the Web. They provide a valuable source of …

In most real-world scenarios, the ultimate goal of recommender system applications is to
suggest a short ranked list of items, namely top-N recommendations, that will appeal to the …

A key issue in group recommendation is how to combine the individual preferences of
different users that form a group and elicit a profile that accurately reflects the tastes of all …

Abstract The World Wide Web is moving from a Web of hyper-linked documents to a Web of
linked data. Thanks to the Semantic Web technological stack and to the more recent Linked …