Catálogo de publicaciones - libros

Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., , , , etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems.

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 space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.

In this chapter we describe techniques for adaptive presentation of content on the Web. We first describe techniques to select and structure the content deemed to be most relevant for the current user in the current interaction context. We then illustrate approaches that deal with the problem of how to adaptively deliver this content.

In recent years, technological developments have made it possible to build interactive 3D models of objects and 3D Virtual Environments that can be experienced through the Web, using common, low-cost personal computers. As in the case of Web-based hypermedia, adaptivity can play an important role in increasing the usefulness, effectiveness and usability of 3D Web sites, i.e., Web sites distributing 3D content. This paper introduces the reader to the concepts, issues and techniques of adaptive 3D Web sites.

This chapter discusses the application of some of the technologies of the adaptive web to the problem of providing information for healthcare consumers. The particular issues relating to this application area are discussed, including the goals of the communication, typical content of a user model, and commonly used techniques. Two case studies are presented, and evaluation approaches considered.

This chapter is about personalization and adaptation in electronic commerce (e-commerce) applications. In the first part, we briefly introduce the challenges posed by e-commerce and we discuss how personalization strategies can help companies to face such challenges. Then, we describe the aspects of personalization, taken as a general technique for the customization of services to the user, which have been successfully employed in e-commerce Web sites. To conclude, we present some emerging trends and and we discuss future perspectives.

In this chapter we discuss various aspects of adaptive mobile guide applications. After having motivated the need for web based mobile applications, we will discuss technologies that are needed to enable adaptive mobile web applications, including not only positioning technologies but also sensor technologies needed to determine additional information on the context and situation of usage. We will also address issues of modeling context and situations before giving an overview on existing systems coming from three important classes of mobile guides: museum guides, navigation systems and shopping assistants. The chapter closes with an extensive discussion of relevant attributes of web based mobile guides.

This chapter describes how the adaptive web technologies discussed in this book have been applied to news access. First, we provide an overview of different types of adaptivity in the context of news access and identify corresponding algorithms. For each adaptivity type, we briefly discuss representative systems that use the described techniques. Next, we discuss an in-depth case study of a personalized news system. As part of this study, we outline a user modeling approach specifically designed for news personalization, and present results from an evaluation that attempts to quantify the effect of adaptive news access from a user perspective. We conclude by discussing recent trends and novel systems in the adaptive news space.

Through interaction with others, a person develops multiple perspectives that become the basis for innovation and the construction of new knowledge. This chapter discusses the challenges facing emerging web-based technologies that enable distributed users to discover and construct new knowledge collaboratively. Examples include advanced collaborative and social information filtering technology that not only helps users discover knowledge, peers, and relevant communities, but also plays a powerful role in facilitating and mediating their interaction. As the internet extends around the world and interconnects diverse cultures, the adaptive web will be challenged to provide a personalized knowledge interface that carries new perspectives to diverse communities. It will play the role of an interface for knowledge construction, a mediator for communication and understanding, and a structured channel through which knowledge is created, interpreted, used, and recreated by other users.

Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user’s preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.