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Personalized article recommendation is important to improve user engagementon news sites. Existing work quantifies engagement primarily through clickrates. We argue that quality of recommendations can be improved byincorporating different types of "post-read" engagement signals like sharing,commenting, printing and e-mailing article links. More specifically, we proposea multi-faceted ranking problem for recommending news articles where each facetcorresponds to a ranking problem to maximize actions of a post-read actiontype. The key technical challenge is to estimate the rates of post-read actiontypes by mitigating the impact of enormous data sparsity, we do so throughseveral variations of factor models. To exploit correlations among post-readaction 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 showthat factor models significantly outperform a few baseline IR models and theLAT model significantly outperforms several other variations of factor models.Our findings show that it is possible to incorporate post-read signals that arecommonly available on online news sites to improve quality of recommendations.

Multi-Faceted Ranking of News Articles using Post-Read Actions