Conditional Cross-Platform User Engagement Prediction

Abstract

The bursting of media sharing platforms like TikTok, YouTube, and Kwai enables normal users to create and share content with worldwide audiences. The most popular YouTuber can attract up to 100 million followers. Since there are multiple popular platforms, it’s quite common that a YouTuber publishes the same media to multiple platforms, or replicates all media from one platform to another. However, the users of different platforms have different tastes. The media that is popular on one platform may not be a great vogue on other platforms. Observing such cross-platform variance, we propose a new task: estimating the user engagement score of a media on one platform given its popularity on other platforms. This task can benefit both the YouTubers and the platform. On one hand, YouTubers can use the predicted engagement to guide the media reworking; on the other hand, the platform can use the predicted engagement to establish promotion and advertising plans. Therefore, this task is of great practical value. To tackle this task, we propose a disentangled neural network that can separate the general media adorability from platform inclinations. In this manner, by substituting the inclination from the source platform to the target platform, we are able to predict the user engagement in the target platform. To validate the proposed model, we manage to build a dataset of micro-videos which are published on four platforms TikTok, Kwai, Bilibili, and WESEE. The experimental results prove the effectiveness of the proposed model.