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

TL;DR

This paper introduces a multi-faceted ranking approach for news article recommendations that incorporates various post-read actions to enhance personalization, using advanced factor models to address data sparsity and improve recommendation quality.

Contribution

The paper proposes a novel multi-faceted ranking framework utilizing factor models and a locally augmented tensor model to incorporate diverse post-read engagement signals in news recommendation.

Findings

Factor models outperform baseline IR models.

LAT model outperforms other factor model variations.

Incorporating post-read signals improves recommendation quality.

Abstract

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.