Leveraging post-click feedback for content recommendations

However, clicks only reflect user preferences according to their first impressions. They do not capture the extent to which users continue to engage with the content.

Our analysis shows that more than half of the clicks on music and short videos are followed by skips from two real-world datasets. In this paper, we leverage post-click feedback, e.g. skips and completions, to improve the training and evaluation of content recommenders. Specifically, we experiment with existing collaborative filtering algorithms and find that they perform poorly against post-click-aware ranking metrics.