Sequential Multi-fusion Network for Multi-channel Video CTR Prediction

In this work, we study video click-through rate (CTR) prediction, crucial for the refinement of video recommendation and the revenue of video advertising. Existing studies have verified the importance of modeling users' clicked items as their latent preference for general click-through rate prediction. However, all of the clicked ones are equally treated in the input stage, which is not the case in online video platforms. This is because each video is attributed to one of the multiple channels (e.g., TV and MOVIES), thus having different impacts on the prediction of candidate videos from a certain channel. To this end, we propose a novel Sequential Multi-Fusion Network (SMFN) by classifying all the channels into two categories: (1) target channel which current candidate videos belong to, and (2) context channel which includes all the left channels. For each category, SMFN leverages a recurrent neural network to model the corresponding clicked video sequence. The hidden interactions between the two categories are characterized by correlating each video of a sequence with the overall representation of another sequence through a simple but effective fusion unit. The experimental results on the real datasets collected from a commercial online video platform demonstrate the proposed model outperforms some strong alternative methods.