  Personalized article recommendation is important to improve user engagement
on news sites. Existing work quantifies engagement primarily through click
rates. We argue that quality of recommendations can be improved by
incorporating different types of "post-read" engagement signals like sharing,
commenting, printing and e-mailing article links. More specifically, we propose
a multi-faceted ranking problem for recommending news articles where each facet
corresponds to a ranking problem to maximize actions of a post-read action
type. The key technical challenge is to estimate the rates of post-read action
types by mitigating the impact of enormous data sparsity, we do so through
several variations of factor models. To exploit correlations among post-read
action types we also introduce a novel variant called locally augmented tensor
(LAT) model. Through data obtained from a major news site in the US, we show
that factor models significantly outperform a few baseline IR models and the
LAT model significantly outperforms several other variations of factor models.
Our findings show that it is possible to incorporate post-read signals that are
commonly available on online news sites to improve quality of recommendations.
