Exploiting Post-Click Behaviors for Recommender System


Hai-bin Zhai, CNCERT/CC


In recent years, recommender systems become an effective technique to address the information overload problem on the Web, targeting the delivery of most relevant items and information to individual users based on their historical behaviours. To build an effective recommender system, implicit feedback like user click has been harvested. Since click data is usually very noisy, recent works also leverage dwell time as a proxy to optimize user engagement. However, dwell time is just one-dimensional post-click behaviour. The multi-dimensional post-click behaviours, e.g., reading comments, browsing images, and other sub-modules, have not been fully exploited. To address this issue, in this paper, we study leveraging post-click behaviours to improve user engagement in recommendation systems. We first take an E-commerce recommender system as an example to define post-click behaviours and demonstrate their effects with respect to user conversions. Based on the analysis, we then propose a tree-based labelling model, which provides a new perspective to understand user engagement beyond CTR or dwell time. The labelling model can be further incorporated into state-of-the-art recommendation methods. Extensive experiments on the dataset from, a real-world E-commerce website, demonstrate the competitive performance of the proposed method in both offline and online scenarios.


Recommender Systems, Post-Click Behaviours, Learning to Rank, Tree-based Model.