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Conditional Cross-Platform User Engagement Prediction by Xinhang Li; Zhaopeng Qiu; Jiacheng Jiang; Yong Zhang; Chunxiao Xing; Xian Wu is a Computer Science article available to read on EtoBox.

What is Conditional Cross-Platform User Engagement Prediction about?

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 inclina

Who reads Conditional Cross-Platform User Engagement Prediction?

Author

Xinhang Li; Zhaopeng Qiu; Jiacheng Jiang; Yong Zhang; Chunxiao Xing; Xian Wu

Publisher

ACM

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)