  We here present a simple and effective model to predict the popularity of web
content. Our solution, which is the winner of two of the three tasks of the
ECML/PKDD 2014 Predictive Analytics Challenge, aims at predicting user
engagement metrics, such as number of visits and social network engagement,
that a web page will achieve 48 hours after its upload, using only information
available in the first hour after upload. Our model is based on two steps. We
first use time series clustering techniques to extract common temporal trends
of content popularity. Next, we use linear regression models, exploiting as
predictors both content features (e.g., numbers of visits and mentions on
online social networks) and metrics that capture the distance between the
popularity time series to the trends extracted in the first step. We discuss
why this model is effective and show its gains over state of the art
alternatives.
