Personalisation in news delivery systems: Item summarization and multi-tier item selection using relevance feedback

• Authors: Alberto Díaz;Pablo Gervás
• Venue: Web Intelligence and Agent Systems
• Year: 2005

The designer of an information dissemination system based on user preferences stated as user models is currently faced with three basic design decisions: whether to use categories, keywords -- or both -- to enable the user to specify his preferences, whether to use a static long-term model or a dynamic short-term model to register those preferences, and what method to use to provide summaries of the available documents without losing information that may be significant to a particular user even if it would not be considered as such in general terms. Current systems tend to provide one specific choice -- either taken at design time by the developer or offered as mutually exclusive alternatives to the user. However, most of the options have relative merits. An efficient way of combining the various solutions would allow users to select in each case the combination of alternatives better suited for their needs. In this paper we defend the use of a combined approach that integrates: an enriched user-model that the user can customise to capture his long-term interests either in terms of categories (newspaper sections) or keywords, a personalised summarization facility to maximise the density of relevance of sent selections, and a tailored relevance feedback mechanism that captures short-term interests as featured in a user's acceptance or rejection of the news items received. Controlled experiments were carried out with a group of users and satisfactory results were obtained, providing material for further development. The experimental results suggest that categories and keywords can be fruitfully combined to express user interests, and that personalised summaries perform better than generic summaries at least in terms of identifying documents that satisfy user preferences.