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Abstract

The potential of summary personalization is high, because a summary that would be useless to decide the relevance of a document if summarized in a generic manner, may be useful if the right sentences are selected that match the user interest. In this paper we defend the use of a personalized summarization facility to maximize the density of relevance of selections sent by a personalized information system to a given user. The personalization is applied to the digital newspaper domain and it used a user-model that stores long and short term interests using four reference systems: sections, categories, keywords and feedback terms. On the other side, it is crucial to measure how much information is lost during the summarization process, and how this information loss may affect the ability of the user to judge the relevance of a given document. The results obtained in two personalization systems show that personalized summaries perform better than generic and generic-personalized summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred direct evaluation that showed a high level of user satisfaction with the summaries.

Type

a

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Abstract

In this paper we are concerned with the type of services that send periodic news selections to subscribers of a digital newspaper by means of electronic mail. The aims are to study the influence of categorisation in information retrieval and in digital newspapers, different models to solve problems of bilingualism in digital information services and to analyse the evaluation in information filtering and personalisation in information agents. Hermes is a multilingual system for the personalisation of news services which allows integration and categorisation of information in two languages. In order to customise information for each user, Hermes provides the means for representing a user interests homogeneously across the operating languages of the system. A simple system is applied to train automatically a dynamic news item classifier for both languages, by taking the Yahoo set of categories as reference framework and using the web pages classified under them as training collection. Traditional evaluation methods have been applied and their shortcomings for the present endeavour have been noted.

Type

a

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Abstract

Some of the most popular measures to evaluate information filtering systems are usually independent of the users because they are based in relevance judgments obtained from experts. On the other hand, the user-centred evaluation allows showing the different impressions that the users have perceived about the system running. This work is focused on discussing the problem of user-centred versus system-centred evaluation of a Web content personalization system where the personalization is based on a user model that stores long term (section, categories and keywords) and short term interests (adapted from user provided feedback). The user-centred evaluation is based on questionnaires filled in by the users before and after using the system and the system-centred evaluation is based on the comparison between ranking of documents, obtained from the application of a multi-tier selection process, and binary relevance judgments collected previously from real users. The user-centred and system-centred evaluations performed with 106 users during 14 working days have provided valuable data concerning the behaviour of the users with respect to issues such as document relevance or the relative importance attributed to different ways of personalization. The results obtained shows general satisfaction on both the personalization processes (selection, adaptation and presentation) and the system as a whole.

Type

a