Filippo Sciarrone

Personalized search on the world wide web

With the exponential growth of the available information on theWorld 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. PersonalizedWeb 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.

Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System

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 VISTATM. 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.

Adaptive Learning with the LS-Plan System: A Field Evaluation

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 learners Cognitive State and Learning Styles, as assessed through prenavigation tests. During the students 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.

Infoweb: An adaptive information filtering system for the cultural heritage domain

This paper presents a system developed for adaptive retrieval and the filtering of documents belonging to digital libraries available on the Web. This system, called InfoWeb, is currently in operation on the ENEA (National Entity for Alternative Energy) digital library Web site reserved to the cultural heritage and environment domain. InfoWeb records the user information needs in a user model, created through a representation, which extends the traditional vector space model and takes the form of a semantic network consisting of co-occurrences between index terms. The initial user model is built on the basis of stereotypes, developed through a clustering of the collection by using specific documents as a starting point. The users query can be expanded in an adaptive way, using the user model formulated by the user himself. The system has been tested on the entire collection comprising about 14,000 documents in HTML/text format. The results of the experiments are satisfactory both in terms of performance and in terms of the systems ability to adapt itself to the users shifting interests.

LS-Plan: An Effective Combination of Dynamic Courseware Generation and Learning Styles in Web-Based Education

This paper presents LS-Plan , a system capable of providing Educational Hypermedia with adaptation and personalization. The architecture of LS-Plan is based on three main components: the Adaptation Engine, the Planner and the Teacher Assistant. Dynamic course generation is driven by an adaptation algorithm, based on Learning Styles, as defined by Felder-Silvermans model. The Planner, based on Linear Temporal Logic, produces a first Learning Objects Sequence, starting from the students Cognitive State and Learning Styles, as assessed through pre-navigation tests. During the students navigation, and on the basis of learning assessments, the adaptation algorithm can propose a new Learning Objects Sequence. In particular, the algorithm can suggest different learning materials either trying to fill possible cognitive gaps or by re-planning a newly adapted Learning Objects Sequence. A first experimental evaluation, performed on a prototype version of the system, has shown encouraging results.

Web document modeling

A very common issue of adaptive Web-Based systems is the modeling of documents. Such documents represent domain-specific information for a number of purposes. Application areas such as Information Search, Focused Crawling and Content Adaptation (among many others) benefit from several techniques and approaches to model documents effectively. For example, a document usually needs preliminary processing in order to obtain the relevant information in an effective and useful format, so as to be automatically processed by the system. The objective of this chapter is to support other chapters, providing a basic overview of the most common and useful techniques and approaches related with document modeling. This chapter describes high-level techniques to model Web documents, such as the Vector Space Model and a number of AI approaches, such as Semantic Networks, Neural Networks and Bayesian Networks. This chapter is not meant to act as a substitute of more comprehensive discussions about the topics presented. Rather, it provides a brief and informal introduction to the main concepts of document modeling, also focusing on the systems that are presented in the rest of the book as concrete examples of the related concepts.

A web-based training system for business letter writing

As with the growing degree of office automation and diffuse use of electronic media, such as e-mails, written business communication is becoming a key element to promote synergies, relationships and disseminating information about products and services. Task recognition and the definition of strategies and suitable vocabularies are some of the activities that office workers deal with each time a communicative intent has to be effectively transferred and understood by a given addressee. This paper introduces a web-based intelligent training system based on the constructivism theory and self-directed learning paradigms for assisting company workers in the drafting business letters-writing task. A case-based engine suggests ad hoc rhetorical letters that users have the chance to adapt to their particular contexts and save them into user-defined case libraries.

Personalized e-learning in Moodle: the Moodle_LS System

Learning Management Systems are among the most popular e-learning tools. Over the last few years, however, scientific research has made considerable progress in developing valuable resources currently unavailable in most Learning Management Systems, including solutions aimed at providing students with personalized support throughout the learning process, which is an essential requirement in continuing education. Observing and modelling the learner, and adapting their learning experience accordingly means opening up new technological and, above all, methodological perspectives in e-learning. The work described in this paper is part of the Open Learning project, in which business-based and university researchers aim to combine the most frequently used e-learning technologies, Learning Management Systems, with the benefits of customized systems so as to develop an innovative learning content delivery system based on the personalization of the learning experience. The proposed system integrates moodle with an engine, LS-Plan, which provides automated sequencing of the learning material based on the learner’s knowledge and learning styles. This paper describes the new system and presents the results of tests conducted in the domain of Italian Neorealist Cinema.

Text categorization in an intelligent agent for filtering information on the web

This paper presents a text categorization system, capable of analyzing HTML/text documents collected from the Web. The system is a component of a more extensive intelligent agent for adaptive information filtering on the Web. It is based on a hybrid case-based architecture, where two multilayer perceptrons are integrated into a case-based reasoner. An empirical evaluation of the system was performed by means of a confidence interval technique. The experimental results obtained are encouraging and support the choice of a hybrid case-based approach to text categorization.

A Teaching-Style Based Social Network for Didactic Building and Sharing

Nowadays, teachers tend to build their own didactic local repository composed by learning objects retrieved from web repositories or, in most cases, by self-made didactic material. In this way they do not share their teaching experience, loosing a precious shortcut to a fast professional update and to an improvement of their teaching activity. In this paper we address the problem of helping teachers to retrieve didac- tic material from a repository through a didactic social network where teachers with similar Teaching Styles, can help each other in retrieving educational material. To this aim a teaching-styles based social network is built following the Grasha TS paradigm. We present a first evaluation of the network embedded in a web application.