Reinforce Lifelong Interaction Value of User-Author Pairs for Large-Scale Recommendation Systems

• URL: http://arxiv.org/abs/2507.16253v1
• Date: Tue, 22 Jul 2025 05:58:55 GMT
• Title: Reinforce Lifelong Interaction Value of User-Author Pairs for Large-Scale Recommendation Systems
• Authors: Yisha Li, Lexi Gao, Jingxin Liu, Xiang Gao, Xin Li, Haiyang Lu, Liyin Hong,
• Abstract summary: We introduce RL to Reinforce Lifelong Interaction Value of User-Author pairs (RLIV-UA) based on each interaction of UA pairs.<n>In offline experiments and online A/B tests, the RLIV-UA model achieves both higher user satisfaction and higher platform profits than compared methods.
• Score: 11.3015594568951
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recommendation systems (RS) help users find interested content and connect authors with their target audience. Most research in RS tends to focus either on predicting users' immediate feedback (like click-through rate) accurately or improving users' long-term engagement. However, they ignore the influence for authors and the lifelong interaction value (LIV) of user-author pairs, which is particularly crucial for improving the prosperity of social community in short-video platforms. Currently, reinforcement learning (RL) can optimize long-term benefits and has been widely applied in RS. In this paper, we introduce RL to Reinforce Lifelong Interaction Value of User-Author pairs (RLIV-UA) based on each interaction of UA pairs. To address the long intervals between UA interactions and the large scale of the UA space, we propose a novel Sparse Cross-Request Interaction Markov Decision Process (SCRI-MDP) and introduce an Adjacent State Approximation (ASA) method to construct RL training samples. Additionally, we introduce Multi-Task Critic Learning (MTCL) to capture the progressive nature of UA interactions (click -> follow -> gift), where denser interaction signals are leveraged to compensate for the learning of sparse labels. Finally, an auxiliary supervised learning task is designed to enhance the convergence of the RLIV-UA model. In offline experiments and online A/B tests, the RLIV-UA model achieves both higher user satisfaction and higher platform profits than compared methods.

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