From Generation to Consumption: Personalized List Value Estimation for Re-ranking

• URL: http://arxiv.org/abs/2508.02242v2
• Date: Thu, 07 Aug 2025 08:51:15 GMT
• Title: From Generation to Consumption: Personalized List Value Estimation for Re-ranking
• Authors: Kaike Zhang, Xiaobei Wang, Xiaoyu Yang, Shuchang Liu, Hailan Yang, Xiang Li, Fei Sun, Qi Cao,
• Abstract summary: We propose CAVE, a personalized Consumption-Aware list Value Estimation framework.<n>CAVE formulates the list value as the expectation over sub-list values, weighted by user-specific exit probabilities at each position.<n>By jointly modeling sub-list values and user exit behavior, CAVE yields a more faithful estimate of actual list consumption value.
• Score: 11.827600847399973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Re-ranking is critical in recommender systems for optimizing the order of recommendation lists, thus improving user satisfaction and platform revenue. Most existing methods follow a generator-evaluator paradigm, where the evaluator estimates the overall value of each candidate list. However, they often ignore the fact that users may exit before consuming the full list, leading to a mismatch between estimated generation value and actual consumption value. To bridge this gap, we propose CAVE, a personalized Consumption-Aware list Value Estimation framework. CAVE formulates the list value as the expectation over sub-list values, weighted by user-specific exit probabilities at each position. The exit probability is decomposed into an interest-driven component and a stochastic component, the latter modeled via a Weibull distribution to capture random external factors such as fatigue. By jointly modeling sub-list values and user exit behavior, CAVE yields a more faithful estimate of actual list consumption value. We further contribute three large-scale real-world list-wise benchmarks from the Kuaishou platform, varying in size and user activity patterns. Extensive experiments on these benchmarks, two Amazon datasets, and online A/B testing on Kuaishou show that CAVE consistently outperforms strong baselines, highlighting the benefit of explicitly modeling user exits in re-ranking.

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