Optimizing the Recency-Relevancy Trade-off in Online News Recommendations.

Online news media sites are emerging as the primary source of news for a large number of users. The selection of 'front-page' stories on these media sites usually takes into consideration several crowdsourced popularity metrics, such as number of views or shares by the readers. In this work, we focus on automatically recommending front-page stories in such media websites. When recommending news stories, there are two basic metrics of interest - recency and relevancy. Ideally, recommender systems should recommend the most relevant stories soon after they are published. However, the relevancy of a story only becomes evident as the story ages, thereby creating a tension between recency and relevancy. A systematic analysis of popular recommendation strategies in use today reveals that they lead to poor trade-offs between recency and relevancy 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 develop an optimization framework combining the predicted future-impact of the stories with the uncertainties in the predictions. Evaluations over three real-world news datasets show that our implementation achieves good performance trade-offs between recency and relevancy.