Recommending Temporally Relevant News Content from Implicit Feedback Data

News has, in this day and age, transformed primarily into a digital format with leading newspapers and news agencies having a significant online presence. The speed at which news reaches the reader notwithstanding, the proliferation of blogs and microblogs to deliver specialized content has become the order of the day. Even highly engaged users tend to disengage with a website when the content they are served is unappealing to them. While recommendation systems have been used to ensure delivery of content to the user in tune with their tastes, these systems face an unprecedented challenge - the transient nature of 'popular' news and users' changing interests. Moreover, the challenge is compounded by the absence of explicit feedback. Most recommendation systems for recommending digital news content rely on inferring user engagement through 'clicks', which is not necessarily an accurate measure as it gives us no explicit information about the degree to which a user is interested in a news article. In this paper, we introduce and study the behavior of temporal and tag-based models for news article recommendation. Our experiments indicate that incorporating temporal and taginformation improves recommendation quality and increases user engagement. We argue through experimental evaluation that the improved performance is due to recommendation of more personalized news content by the tag-based recommendation algorithms as compared to other models that do not explicitly incorporate user-tag information.