Learning to model relatedness for news recommendation

Yuanhua Lv, Taesup Moon, Pranam Kolari, Zhaohui Zheng, Xuanhui Wang, Yi Chang
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
more » ... ly 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.
doi:10.1145/1963405.1963417 dblp:conf/www/LvMKZWC11 fatcat:3tfk3arelrfibneh3h6tgvtdqa