Foresight Prediction Enhanced Live-Streaming Recommendation
Jiangxia Cao, Ruochen Yang, Xiang Chen, Changxin Lao, Yueyang Liu, Yusheng Huang, Yuanhao Tian, Xiangyu Wu, Shuang Yang, Zhaojie Liu, Guorui Zhou

TL;DR
This paper introduces a foresight prediction method for live-streaming recommendation that models content evolution to improve real-time content relevance and user engagement.
Contribution
It proposes a novel approach to predict future live-streaming content by semantic quantization and trend modeling, enhancing recommendation accuracy.
Findings
Improved user engagement during highlight moments
Enhanced recommendation relevance through foresight prediction
Effective in both offline and online experiments
Abstract
Live-streaming, as an emerging media enabling real-time interaction between authors and users, has attracted significant attention. Unlike the stable playback time of traditional TV live or the fixed content of short video, live-streaming, due to the dynamics of content and time, poses higher requirements for the recommendation algorithm of the platform - understanding the ever-changing content in real time and push it to users at the appropriate moment. Through analysis, we find that users have a better experience and express more positive behaviors during highlight moments of the live-streaming. Furthermore, since the model lacks access to future content during recommendation, yet user engagement depends on how well subsequent content aligns with their interests, an intuitive solution is to predict future live-streaming content. Therefore, we perform semantic quantization on…
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Taxonomy
TopicsRecommender Systems and Techniques · Image and Video Quality Assessment · Video Analysis and Summarization
