iScore: Measuring the Interestingness of Articles in a Limited User Environment

Raymond K. Pon, Alfonso F. Cardenas, David J. Buttler, Terence J. Critchlow
2007 2007 IEEE Symposium on Computational Intelligence and Data Mining  
Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevancy scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation
more » ... recommendation systems, in a limited user environment there are not enough users that would make collaborative filtering effective. We present a general framework for defining and measuring the "interestingness" of articles, incorporating user-feedback. We show 21% improvement over traditional IR methods.
doi:10.1109/cidm.2007.368896 dblp:conf/cidm/PonCBC07 fatcat:43qiofknfffn5kjciihefeft6q