Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the s...

Since more and more Web sites, especially sites of retailers, offer automatic recommendation services using Web usage mining, evaluation of recommender algorithms has become increasingly important. In this paper we present a framework for the evaluation of different aspects of recommender systems based on the process of discovering knowledge in databases introduced by Fayyad et al. and we summa...

Recommender systems are frequently used as part of online shops to help consumers browse through large product offerings by recommending those products which are the most relevant for them. Although consumers’ interactions with recommender systems have been subject to substantial research, it is still unclear what the effect on aggregated sales diversity is, i.e. whether this leads to predomina...

Many efforts have been done to tackle the problem of information abundance in the World Wide Web. Growth in the number of web users and the necessity of making the information available on the web, make web recommender systems very critical and popular. Recommender systems use the knowledge obtained through the analysis of users' navigational behavior, to customize a web site to the needs of ea...

Background: Nutrition informatics has become a novel approach for registered dietitians to practice in this field and make a profit for health care. Recommendation systems considered as an effective technology into aid users to adjust their eating behavior and achieve the goal of healthier food and diet. The purpose of this study is to review nutrition recommendation systems (NRS) and their cha...

We present News@hand, a news recommender system which applies Semantic Web technologies to describe and relate news contents and user preferences in order to produce enhanced recommendations. The multi-domain portability, the multi-media source applicability, and the addressing of some limitations of current recommender systems are the main benefits of our proposal.

With the retail electronic commerce being a major global shopping phenomenon, retailers need to develop additional tools to improve their sales. One such tool is a Recommender System through which the shopping page recommends products to the shoppers using their past Web shopping and product search behavior. While recommender systems are common, few studies exist regarding their usability and u...

The amount of data on the web is constantly growing. The separation of relevant from less important information is a challenging task. Due to the huge amount of data available in the World Wide Web, the processing cannot be done manually. Software components are needed that learn the user preferences and support users in finding the relevant information. In this work we present our recommender ...

As it is known the Web is changing the information access processes. The Web is one of the most important information media. Furthermore, the Web is influencing in the development of other information media, as for example, newspapers, journals, books, libraries, etc. In this chapter we analyze its impact in the development of the University Digital Libraries (UDL). As in the Web, the growing o...

Personalization technologies have been popular in assisting users with the information overload problem. As the number of services and the volume of content continues to grow personalization technologies are more than ever in demand. Mobasher (Mobasher et al., 2004) classifies web personalization into 3 groups. These are, manual decision rule systems, content-based recommender systems and colla...