document: Tracking multiple topics for finding interesting articles We introduce multiple topic tracking ( MTT ) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time . As an extension of the basic Rocchio algorithm , traditional topic detection and tracking , and single-pass clustering , MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback . Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy . Also by relating a topic 's interestingness to an article . s interestingness , iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm . We identify several operating parameters that work well for MTT . Using the same parameters , we show that MTT alone yields high quality results for recommending interesting articles from several corpora . The inclusion of MTT improves iScore 's performance by 9 % in recommending news articles from the Yahoo ! News RSS feeds and the TREC11 adaptive filter article collection . And through a small user study , we show that iScore can still perform well when only provided with little user feedback .
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