Comprehensive List Generation for Multi-Generator Reranking

• URL: http://arxiv.org/abs/2504.15625v1
• Date: Tue, 22 Apr 2025 06:34:57 GMT
• Title: Comprehensive List Generation for Multi-Generator Reranking
• Authors: Hailan Yang, Zhenyu Qi, Shuchang Liu, Xiaoyu Yang, Xiaobei Wang, Xiang Li, Lantao Hu, Han Li, Kun Gai,
• Abstract summary: We show that we can achieve a more efficient and effective list proposal with a multi-generator framework.<n>We design an automatic complementary generator-finding framework that learns a policy that simultaneously aligns the users' preferences and maximizes the list comprehensiveness metric.
• Score: 19.54795824992667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reranking models solve the final recommendation lists that best fulfill users' demands. While existing solutions focus on finding parametric models that approximate optimal policies, recent approaches find that it is better to generate multiple lists to compete for a ``pass'' ticket from an evaluator, where the evaluator serves as the supervisor who accurately estimates the performance of the candidate lists. In this work, we show that we can achieve a more efficient and effective list proposal with a multi-generator framework and provide empirical evidence on two public datasets and online A/B tests. More importantly, we verify that the effectiveness of a generator is closely related to how much it complements the views of other generators with sufficiently different rerankings, which derives the metric of list comprehensiveness. With this intuition, we design an automatic complementary generator-finding framework that learns a policy that simultaneously aligns the users' preferences and maximizes the list comprehensiveness metric. The experimental results indicate that the proposed framework can further improve the multi-generator reranking performance.

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