Personal information agents emerged in the last decade as an alternative to assist users to cope with the increasing volume of information available on the Web. In order to provide personalized assistance, these agents rely on user profiles modeling user information preferences, interests and habits. Inserted in communities of people with similar interests, personal agents can improve their ass...

One of the most important class of Data Mining applications is the so called “Web Mining Systems” which analyzes and extracts important and non-trivial knowledge from Web related data. Typical applications of Web Mining are represented by the personalization or recommender systems. These systems are aimed to extract knowledge from the analysis of historical information of a web server in order ...

This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering a...

Recommender systems generate responses and suggest items in the required domain. This paper proposes a domain independent trust based recommender system where personalized recommendations can be generated for any domain known to the recommenders. In this recommender system, there exists a web of trust which is formed on the basis of trust among agents in application domain. Here each user captu...

Collaboration, interaction and information sharing are the main driving forces of the current generation of web applications referred to as ‘Web 2.0’ [47]. Well-known examples of this emerging trend include weblogs (online diaries or journals for sharing ideas instantly), Friend-Of-A-Friend1 (FOAF) files (machine-readable documents describing basic properties of a person, including links betwee...

Low back pain affects a large proportion of the adult population at some point in their lives and has a major economic and social impact. To soften this impact, one possible solution is to make use of recommender systems, which have already been introduced in several health fields. In this paper, we present TPLUFIB-WEB, a novel fuzzy linguistic Web system that uses a recommender system to provi...

The motivation behind personal information agents resides in the enormous amount of information available on the Web, which has created a pressing need for effective personalized techniques. In order to assists Web search these agents rely on user profiles modeling information preferences, interests and habits that help to contextualize user queries. In communities of people with similar intere...

Medical tourism has been growing rapidly in recent years. This trend causing the information about medical tourism destination will increase significantly. The information of medial tourism has been found online started from the demographic spread of the potential medical tourists and destination. However, the growth of information available on the web nowadays has led to information overload, ...

This paper describes the design and implementation of a recommender application framework which aims to simplify development of ontology-based recommender applications over the Semantic Web data. Recommender system is a type of system that generates meaningful recommendations to support user’s decision. Development of recommender system for the Semantic Web data typically requires ontology, rul...

From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different online learning platforms and delivers personalized recommendation. The underlying educational objective is to enable academic institutions to...