让每类商品在推荐中获得公平曝光，兼顾推荐质量与实时性。
Producer-Fairness in Sequential Bundle Recommendation
- 定义生产者公平性，确保不同商品组在推荐中均衡曝光。
- 提出精确解与三种启发式算法，实现实时推荐决策。
- 在三个真实数据集上验证效果，兼顾公平与推荐质量。
我们研究顺序捆绑推荐中的公平性问题，即用户依次接收一组相关且兼容的商品。受真实场景启发，我们形式化了生产者公平性，旨在推荐会话中实现不同商品组的合理曝光。该目标可自然融入高质量捆绑构建过程。问题在用户到达时实时求解。我们提出适用于小规模实例的精确解法，并考察两种启发式策略：质量优先与公平优先，以及一种动态平衡公平与质量的自适应变体。在三个真实世界数据集上的实验揭示了各方法的优势与局限，证实其在不牺牲捆绑质量的前提下提供公平推荐的有效性。
原文摘要 · Abstract (English)
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.
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