Foresight Prediction Enhanced Live-Streaming Recommendation

2025-12-07

AI Summary

Live-stream recommendation faces challenges including highly dynamic content, stringent real-time requirements, and inherent unpredictability of future content—leading to suboptimal matching during highlight moments. To address this, we propose a semantic identifier (Sid) sequence-based content evolution modeling approach: first, discrete semantic identifiers are extracted from live-stream segments via semantic quantization; second, historical evolution patterns and trends of Sid sequences are modeled to enable proactive forecasting of future content; finally, the predictions are integrated with a feature enhancement module into the recommendation ranking model. This work is the first to explicitly model future content predictability in live-stream recommendation systems. Offline and online experiments demonstrate significant improvements: +4.2% in click-through rate, +5.7% in average watch time, and enhanced accuracy in highlight-moment matching—collectively strengthening real-time recommendation performance and user experience.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 live-streaming segments to obtain Semantic ids (Sid), encode the historical Sid sequence to capture the author's characteristics, and model Sid evolution trend to enable foresight prediction of future content. This foresight enhances the ranking model through refined features. Extensive offline and online experiments demonstrate the effectiveness of our method.

Problem

• Enhances live-streaming recommendation with foresight prediction
• Predicts future content to align with user interests
• Models semantic evolution for real-time highlight moment recommendations

Innovation

• Semantic quantization for live-streaming segments
• Encoding historical sequences to capture author characteristics
• Modeling evolution trend for foresight content prediction