Knowing User Better: Jointly Predicting Click-Through and Playtime for Micro-Video

摘要

Most micro-video recommender systems use the click-through to measure user satisfaction. However, the amount of time that users spend on a video, the playtime, measures user engagement on video contents and should be used as a complement to the click based signals. In this paper, we propose a coarse-to-fine multi-task jointly optimizing model to predict click-through and playtime. Following the click-through prediction, the playtime is first discretized into several intervals and classified into a specific one with a proposed ordered-balanced cross entropy loss. Then, to further improve upon coarse estimates, we learn a subtle offset with a regressor and produce a fine-grained playtime estimation. To make mutual promotion between click-through and playtime predictors, we optimize them jointly in a multi-task manner. Experimental results show that we achieve state-of-the-art performance on recommendation task and demonstrate effectiveness on playtime prediction at the same time.