Abstract.With the exponential growth of the available information on the World Wide Web, a traditional search engine, even if based on sophisticated document indexing algorithms, has difficulty meeting efficiency and effectiveness performance demanded by users searching for relevant information. Users surfing the Web in search of resources to satisfy their information needs have less and less time and patience to formulate queries, wait for the results and sift through them. Consequently, it is vital in many applications -for example in an e-commerce Web site or in a scientific one -for the search system to find the right information very quickly. Personalized Web environments that build models of short-term and long-term user needs based on user actions, browsed documents or past queries are playing an increasingly crucial role: they form a winning combination, able to satisfy the user better than unpersonalized search engines based on traditional Information Retrieval (IR) techniques. Several important user personalization approaches and techniques developed for the Web search domain are illustrated in this chapter, along with examples of real systems currently being used on the Internet. IntroductionRecently, several search tools for the Web have been developed to tackle the information overload problem, that is, the over-abundance of resources that prevent the user from retrieving information solely by navigating through the hypertextual space. Some make use of effective personalization, adapting the results according to each user's information needs. This contrasts with traditional search engines that return the same result list for the same query, regardless of who submitted the query, in spite of the fact that different users usually have different needs. In order to incorporate personalization into full-scale Web search tools, we must study the behavior of the users as they interact with information sources.
LS-Plan is a framework for personalization and adaptation in e-learning. In such framework an Adaptation Engine plays a main role, managing the generation of personalized courses from suitable repositories of learning nodes and ensuring the maintenance of such courses, for continuous adaptation of the learning material proposed to the learner. Adaptation is meant, in this case, with respect to the knowledge possessed by the learner and her learning styles, both evaluated prior to the course and maintained while attending the course. Knowledge and Learning styles are the components of the student model managed by the framework. Both the static, precourse, and dynamic, in-course, generation of personalized learning paths are managed through an adaptation algorithm and performed by a planner, based on Linear Temporal Logic. A first Learning Objects Sequence is produced based on the initial learner's Cognitive State and Learning Styles, as assessed through prenavigation tests. During the student's navigation, and on the basis of learning assessments, the adaptation algorithm can output a new Learning Objects Sequence to respond to changes in the student model. We report here on an extensive experimental evaluation, performed by integrating LS-Plan in an educational hypermedia, the LECOMPS web application, and using it to produce and deliver several personalized courses in an educational environment dedicated to Italian Neorealist Cinema. The evaluation is performed by mainly following two standard procedures: the As a Whole and the Layered approaches. The results are encouraging both for the system on the whole and for the adaptive components
A case study in adaptive information filtering systems for the Web is presented. The described system comprises two main modules, named HUMOS and WIFS. HUMOS is a user modeling system based on stereotypes. It builds and maintains long term models of individual Internet users, representing their information needs. The user model is structured as a frame containing informative words, enhanced with semantic networks. The proposed machine learning approach for the user modeling process is based on the use of an artificial neural network for stereotype assignments. WIFS is a content-based information filtering module, capable of selecting html/text documents on computer science collected from the Web according to the interests of the user. It has been created for the very purpose of the structure of the user model utilized by HUMOS. Currently, this system acts as an adaptive interface to the Web search engine ALTA VISTA™. An empirical evaluation of the system has been made in experimental settings. The experiments focused on the evaluation, by means of a non-parametric statistics approach, of the added value in terms of system performance given by the user modeling component; it also focused on the evaluation of the usability and user acceptance of the system. The results of the experiments are satisfactory and support the choice of a user model-based approach to information filtering on the Web
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