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 …

In this age of information overload, people use a variety of strategies to make choices about
what to buy, how to spend their leisure time, and even whom to date. Recommender …

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 …

Today recommenders are commonly used with various purposes, especially dealing with e-
commerce and information filtering tools. Content-based recommenders rely on the concept …

Recommender systems have been developed in variety of domains, including
asynchronous discussion group which is one of the most interesting ones. Due to the …

Collaborative and content-based filtering are the recommendation techniques most widely
adopted to date. Traditional collaborative approaches compute a similarity value between …

Basic content personalization consists in matching up the attributes of a user profile, in
which preferences and interests are stored, with the attributes of a content object. The Web …

This paper brings together research from two different fields–user modelling and web
ontologies–in attempt to demonstrate how recent semantic trends in web development can …

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 …

This paper presents a movie recommendation system according to scores that the users
provide. In view of the movie evaluation system, the impacts of access control and …