社交电商平台中的公平分配研究
Towards Fair Allocation in Social Commerce Platforms
AI总结：
针对社交电商平台的产品分配公平性问题，将其建模为双边基数约束下的不可分割物品公平分配问题，并提出混合整数线性规划与启发式方法实现Nash社会福祉的近最优近似。
摘要：
社交电商平台是一种新兴商业模式，生产商通过转售商将产品推销给其社交网络中的其他客户。由于该模式日益普及，数千名小型生产商和转售商开始依赖这些平台维持生计，因此为他们提供公平的赚钱机会至关重要。此类平台庞大的产品空间使得人工搜索变得不可行，促使需要推荐算法来有效分配产品曝光度，进而分配赚钱机会。在本研究中，我们关注社交电商平台中此类分配的公平性，并将向转售商分配产品的问题表述为在双边基数约束下包含不可分割物品的公平分配问题，其中每个产品必须分配给至少一定数量的转售商，且每个转售商必须获得一定数量的产品。我们的工作从理论和实验两个角度系统地探讨了多种被充分研究的公平性基准——包括Nash社会福祉、至多嫉妒一件物品（EF1）和至多等价一件物品（EQ1）。我们发现，在无约束环境下已知的这些概念的存在性与计算保证并不能扩展到我们的受约束模型中。为了解决这一局限性，我们开发了一种混合整数线性规划及其他可扩展的启发式方法，在模拟和真实的社交电商数据集上实现了对Nash社会福祉的近最优近似。总体而言，我们的工作为在社交电商平台上实现可证明的公平性以及合理的收入保证迈出了第一步。
英文摘要：
Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products. Our work systematically explores various well-studied benchmarks of fairness -- including Nash social welfare, envy-freeness up to one item (EF1), and equitability up to one item (EQ1) -- from both theoretical and experimental perspectives. We find that the existential and computational guarantees of these concepts known from the unconstrained setting do not extend to our constrained model. To address this limitation, we develop a mixed-integer linear program and other scalable heuristics that provide near-optimal approximation of Nash social welfare in simulated and real social commerce datasets. Overall, our work takes the first step towards achieving provable fairness alongside reasonable revenue guarantees on social commerce platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
