research-article

Conditional Cross-Platform User Engagement Prediction

Authors:

Jiacheng Jiang,

Xian WuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 1

Article No.: 6, Pages 1 - 28

https://doi.org/10.1145/3589226

Published: 18 August 2023 Publication History

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.

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Cited By

Li XZhao XWang YLiu YChen CLong CZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)OpenSiteRec: An Open Dataset for Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657875(1483-1493)Online publication date: 10-Jul-2024
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Li XZhao XWang ZDuan YZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Optimal Transport Enhanced Cross-City Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657757(1441-1451)Online publication date: 10-Jul-2024
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Zheng ZHu XQiu ZCheng YGao SSong YZhu HXiong H(2024)Bilateral Multi-Behavior Modeling for Reciprocal Recommendation in Online RecruitmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339770536:11(5681-5694)Online publication date: Nov-2024
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Index Terms

Conditional Cross-Platform User Engagement Prediction
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 1

January 2024

924 pages

EISSN:1558-2868

DOI:10.1145/3613513

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 18 August 2023

Online AM: 24 March 2023

Accepted: 09 March 2023

Revised: 08 December 2022

Received: 16 May 2022

Published in TOIS Volume 42, Issue 1

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Cited By

Li XZhao XWang YLiu YChen CLong CZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)OpenSiteRec: An Open Dataset for Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657875(1483-1493)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657875
Li XZhao XWang ZDuan YZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Optimal Transport Enhanced Cross-City Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657757(1441-1451)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657757
Zheng ZHu XQiu ZCheng YGao SSong YZhu HXiong H(2024)Bilateral Multi-Behavior Modeling for Reciprocal Recommendation in Online RecruitmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339770536:11(5681-5694)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3397705

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