Multi-faceted ranking of news articles using post-read actions

Personalized article recommendation is important for news portals to improve user engagement. Existing work quantifies engagement primarily through click rates. We suggest that quality of recommendations may be improved by exploiting different types of "post-read" engagement signals like sharing, commenting, printing and e-mailing article links. Specifically, we propose a multi-faceted ranking problem for recommending articles, where each facet corresponds to a ranking task that seeks to maximize actions of a particular post-read type (e.g., ranking articles to maximize sharing actions). Our approach is to predict the probability that a user would take a post-read action on an article, so that articles can be ranked according to such probabilities. However, post-read actions are rare events --- enormous data sparsity makes the problem challenging. We meet the challenge by exploiting correlations across different post-read action types through a novel locally augmented tensor (LAT) model, so that the ranking performance of a particular action type can be improved by leveraging data from all other action types. Through extensive experiments, we show that our LAT model significantly outperforms a variety of state-of-the-art factor models, logistic regression and IR models.