Automatic adaptation and recommendation of news reports using surface-based methods

Abstract

The multitude of news reports being published on the WWW may cause information overload on users. In this paper, we describe a news recommendation system whereby news reports are represented using entity-relationship graphs, and the users’ interaction with these news reports in a specialised web portal is monitored in order to construct and maintain user models that store the user’s reading history and also define entities that appear to be of interest to the user. These user models are used to alert individual users when an event has occurred that falls within their area of interest, and to present news reports to users in an adaptive manner – previously seen information is shown in a summarised form. We evaluated our recommendation system using a corpus of news reports downloaded from Yahoo! News. Results obtained indicate that our recommendation system performs better than the baseline system that uses the Rocchio algorithm without negative feedback.