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Learning to model relatedness for news recommendation
2011
Proceedings of the 20th international conference on World wide web - WWW '11
With the explosive growth of online news readership, recommending interesting news articles to users has become extremely important. While existing Web services such as Yahoo! and Digg attract users' initial clicks by leveraging various kinds of signals, how to engage such users algorithmically after their initial visit is largely under explored. In this paper, we study the problem of post-click news recommendation. Given that a user has perused a current news article, our idea is to
doi:10.1145/1963405.1963417
dblp:conf/www/LvMKZWC11
fatcat:3tfk3arelrfibneh3h6tgvtdqa