document: Learning to model relatedness for news recommendation 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 automatically identify `` related '' news articles which the user would like to read afterwards . Specifically , we propose to characterize relatedness between news articles across four aspects : relevance , novelty , connection clarity , and transition smoothness . Motivated by this understanding , we define a set of features to capture each of these aspects and put forward a learning approach to model relatedness . In order to quantitatively evaluate our proposed measures and learn a unified relatedness function , we construct a large test collection based on a four-month commercial news corpus with editorial judgments . The experimental results show that the proposed heuristics can indeed capture relatedness , and that the learned unified relatedness function works quite effectively .
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