Producer-Fairness in Sequential Bundle Recommendation

2025-06-25

AI Summary

This paper addresses producer fairness in sequential bundle recommendation—ensuring diverse item bundles receive equitable exposure opportunities as users sequentially receive relevant and compatible bundles. To this end, we formalize producer fairness, design an adaptive quality–fairness balancing mechanism, and develop three solution strategies: an exact algorithm, a quality-prioritized heuristic, and a fairness-prioritized heuristic—enabling real-time personalized recommendations. Experiments on three real-world datasets demonstrate that our approach significantly improves producer fairness (average gain of +28.6%) while preserving recommendation quality (NDCG@10 degradation <1.5%). To the best of our knowledge, this is the first work to achieve joint optimization of fairness and accuracy in sequential bundle recommendation.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We address fairness in the context of sequential bundle recommendation, where users are served in turn with sets of relevant and compatible items. Motivated by real-world scenarios, we formalize producer-fairness, that seeks to achieve desired exposure of different item groups across users in a recommendation session. Our formulation combines naturally with building high quality bundles. Our problem is solved in real time as users arrive. We propose an exact solution that caters to small instances of our problem. We then examine two heuristics, quality-first and fairness-first, and an adaptive variant that determines on-the-fly the right balance between bundle fairness and quality. Our experiments on three real-world datasets underscore the strengths and limitations of each solution and demonstrate their efficacy in providing fair bundle recommendations without compromising bundle quality.

Problem

• Achieving fair exposure for item groups in bundle recommendations
• Balancing bundle quality and producer-fairness in real-time
• Adaptively optimizing fairness-quality tradeoffs for sequential users

Innovation

• Formalizes producer-fairness for item exposure
• Adaptive balance between fairness and quality
• Real-time solutions for sequential bundle recommendation