Optimizing the recency-relevance-diversity trade-offs in non-personalized news recommendations

Online news media sites are emerging as the primary source of news for a large number of users. Due to a large number of stories being published in these media sites, users usually rely on news recommendation systems to find important news. In this work, we focus on automatically recommending news stories to all users of such media websites, where the selection is not influenced by a particular user’s news reading habit. When recommending news stories in such non-personalized manner, there are three basic metrics of interest—recency, importance (analogous to relevance in personalized recommendation) and diversity of the recommended news. Ideally, recommender systems should recommend the most important stories soon after they are published. However, the importance of a story only becomes evident as the story ages, thereby creating a tension between recency and importance. A systematic analysis of popular recommendation strategies in use today reveals that they lead to poor trade-offs between recency and importance in practice. So, in this paper, we propose a new recommendation strategy (called Highest Future-Impact) which attempts to optimize on both the axes. To implement our proposed strategy in practice, we propose two approaches to predict the future-impact of news stories, by using crowd-sourced popularity signals and by observing editorial selection in past news data. Finally, we propose approaches to inculcate diversity in recommended news which can maintain a balanced proportion of news from different news sections. Evaluations over real-world news datasets show that our implementations achieve good performance in recommending news stories.

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