Comprehensive List Generation for Multi-Generator Reranking

2025-04-22

AI Summary

To address insufficient diversity and incomplete coverage of user preferences in multi-generator re-ranking, this paper proposes a comprehensiveness-driven collaborative re-ranking framework. Methodologically, we first formally define and quantify “list comprehensiveness,” then formulate a joint optimization objective balancing preference alignment and comprehensiveness maximization; we further design a learnable complementarity assessment module to enable automatic generator discovery and collaborative scheduling. Our contributions are threefold: (1) the first comprehensiveness metric and optimization paradigm tailored for re-ranking; (2) a learnable modeling mechanism for generator complementarity; and (3) significant improvements in NDCG (+2.1%) and CTR (+1.8%) on two public benchmarks and online A/B tests, empirically validating the framework’s effectiveness in enhancing both recommendation quality and coverage breadth.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Combinatorial Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

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.

Problem

• Improving recommendation lists by multi-generator competition
• Enhancing list comprehensiveness via complementary generators
• Optimizing user preferences and list diversity simultaneously

Innovation

• Multi-generator framework enhances list proposal efficiency
• Automatic complementary generator-finding aligns user preferences
• List comprehensiveness metric improves reranking performance