Past evidence has shown that generic approaches to recommender systems based upon collaborative filtering tend to poorly scale. Moreover, their fitness for scenarios supposing distributed data storage and decentralized control, like the Semantic Web, becomes largely limited for various reasons. We believe that computational trust models bear several favorable properties for social filtering, op...

The increasing relevance assumed by the E-Commerce in the Web community is attested by the great number of powerful and sophisticated tools developed in the last years to support traders in their commercial activities. In this scenario, recommender systems appear doubtless as a promising solution for supporting both customers’ and merchants’ activities. In this paper, we propose an agent-based ...

This paper presents a web based recommender system aimed at supporting a user in information filtering and product bundling. The system enables the selection of travel locations, activities and attractions, and supports the bundling of a personalized travel plan. A travel plan is composed in a mixed initiative way: the user poses queries and the recommender exploits an innovative technology tha...

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filteri...

This work explores the use of Semantic Web techniques as an alternative to traditional educational recommender systems. The proposed model explores the use of Learning Object metadata information and Student academic profile described by knowledge ontologies in order to provide a knowledge structure. This knowlege is stored in an OWL ontology providing domain knowledge and knowledge interoperab...

Recommender systems are one of the information filtering systems which provides business intelligence to the active users on e-commerce by more suggesting suitable items through personalized web page. User’s explicit knowledge about the items, preferences, density of neighbours and recommendation criteria play vital role in providing accurate recommendations. This paper introduces knowledge and...

Nowadays, users are exposed to an overwhelming amount of information in several application domains. Recommender systems fight such an overload by selecting the products which are more appealing to each user, according to his personal preferences or needs. Current personalization techniques are based on more or less sophisticated syntactic methods which miss a lot of knowledge during the elabor...

Traditionally, recommender systems exploit user ratings to infer preferences. However, the growing popularity of social platforms has encouraged users to write textual reviews about liked items. These reviews represent a valuable source of non-trivial information that could improve users’ decision processes. In this paper we propose a novel recommendation approach based on the semantic annotati...

Ontology visualization is an important functionality for ontology users. Since many visualization methods have been proposed and implemented, it is not an easy task for an ontology user to select a proper ontology visualization tool based on the user’s requirements. In order to recommend an ontology visualization tool, some requirements should be provided manually by the user. However, other re...