Development of Web 2.0 enabled users to share information online, which results into an exponential growth of world wide web data. This leads to the so-called information overload problem. Recommender Systems (RS) are intelligent systems, helping on-line users to overcome information overload by providing customized recommendations on various items. In real world, people are willing to take adv...

Case-based recommender systems have been successfully applied to tourism web sites for suggesting to travellers products they might like such as hotels or events. Since they exploit previous experiences by other travellers (cases), their casebase needs to be bootstrapped at deploy time by inserting initial experiences. In this paper we address this open problem and propose a methodology for boo...

Web usage mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. The motive of mining is to find users’ access models automatically and quickly from the vast Web log data, such as frequent access paths, frequent access page groups and user clustering. Through web usage mining, the server log, registration information and other rel...

Web recommender systems anticipate the information needs of on-line users and provide them with recommendations to facilitate and personalize their navigation. There are many approaches to building such systems. Among them, using web access logs to generate users’ navigational models capable of building a web recommender system is a popular approach, given its non-intrusiveness. However, using ...

As the information on the Web grows, the need of recommender systems to ease user navigations becomes evident. There exist many approaches of learning for Web usage based recommender systems. In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing c...

Recommender systems suggest objects to users navigating a web site. They observe the pages that a user visits and predict which other pages may be of interest. On the basis of these predictions recommenders select a number of pages that are suggested to the user. By far the most popular recommendation strategy is to select the pages of which the recommender believes they are the most interestin...

Purpose – A good recommender system helps users find items of interest on the web and can provide recommendations based on user preferences. In contrast to automatic technology-generated recommender systems, this paper aims to use dynamic expert groups that are automatically formed to recommend domain-specific documents for general users. In addition, it aims to test several effectiveness measu...

Web recommender systems anticipate the information needs of on-line users and provide them with recommendations to facilitate and personalize their navigation. There are many approaches to build such systems. Among them, using web access logs to generate users’ navigational models to build a web recommender system is a popular approach, given its non-intrusiveness. However, using only one infor...

Recommender Systems have become a significant area in the context of web personalization, given the large amount of available data. Ontologies can be widely taken advantage of in recommender systems, since they provide a means of classifying and discovering of new information about the items to recommend, about user profiles and even about their context. We have developed a semantically enhance...

Abstract. Research on recommender systems has primarily addressed centralized scenarios and largely ignored open, decentralized systems where remote information distribution prevails. The absence of superordinate authorities having full access and control introduces some serious issues requiring novel approaches and methods. Hence, our primary objective targets the successful deployment and int...