Dynamic personalized recommendation of comment-eliciting stories.

Media Websites often solicit users' comments on content items such as videos, news stories, blog posts, etc. Commenting activity increases user engagement with the sites, by both comment writers and readers, and so sites are looking for ways to increase the volume of comments. This work develops a recommender system aiming to present users with items -- news stories, in our case -- on which they are likely to comment. We combine items' content with a collaborative-filtering approach (utilizing users' co-commenting patterns) in a latent factor modeling framework. Building upon previous work, we focus on a continuous, real-time approach to address the problem above. After an initial training period during which commenting activity of users is observed, the system is tested at each subsequent comment submission event by predicting which story is being commented on by a given user at a given time. Our results show that we are able to overcome the site's inherent presentation bias and outperform a strong baseline as users' commenting history grows.