1,721,092 research outputs found
Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System
No full textA 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
A Hybrid Architecture for the Recognition of User Interests during Hypermedia Navigation
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Personalized Search based on a Memory Retrieval Theory
No full textPersonalization is the ability to retrieve information content related to users' profile and facilitate their information-seeking activities. Several environments, such as the Web, take advantage of personalization techniques because of the large amount of available information. For this reason, there is a growing interest in providing automated personalization processes during the human-computer interaction. In this paper we introduce a new approach for user modeling, which grounds in the Search of Associative Memory (SAM) theory. By means of implicit feedback techniques, the approach is able to unobtrusively recognize user needs and monitor the user working context in order to provide important information useful to personalize traditional search tools and implement recommender systems. Experimental results based on precision and recall measures indicate improvements in comparison with traditional user models
Text Categorization with Modified LSI
No full textAutomatic Text Categorization (TC) is a complex and useful task for many naturallanguage applications, and is usually performed through the use of a set of manuallyclassified documents, a training collection. Term-based representation of documents hasfound widespread use in TC. However, one of the main shortcomings of such methods isthat they largely disregard lexical semantics and, as a consequence, are not sufficientelyrobust with respect to variations in word usage. We shall design, implement, and evaluatea new, text classification algorithm. Our main idea is to find a series of projections ofthe training data by using a new modifided LSI algorithm, project all training instancesto the low-dimensional subspace found in the previous step, induce a binary search onthe projected low-dimensional data. Our conclusion is that, with all its simplicity andefficiency, our approach is comparable (and sometimes superior) to SVM in terms ofaccurac
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