LORE: a large-scale offer recommendation engine with eligibility and capacity constraints
LORE: a large-scale offer recommendation engine with eligibility and capacity constraints
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LORE：具有资格和容量限制的大规模报价推荐引擎
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Yi Liu
中科院分区：
文献类型：
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作者：
Rahul M. Makhijani;Shreya Chakrabarti;Dale Struble;Yi Liu
亚马逊、百货商店连锁店、家居用品连锁店、优步和Lyft等企业经常提供优惠、产品折扣和激励措施，以推动销售、提高新产品的接受度并吸引用户。为了吸引不同的用户群体，这些企业通常会设计多个促销活动，但向不同的用户推销不同的促销活动。例如，优步向一些用户提供乘车服务的百分比折扣，向其他用户提供较低的固定价格。在本文中，我们提出了同时受用户资格和物品或优惠能力(有限数量的物品或优惠)约束的最优推荐促销和物品的解决方案，以最大化用户转化率。我们通过基于最小费用流网络优化的报价推荐模型来实现这一点，该模型使我们能够满足优化本身的约束并在多项式时间内求解。我们提出了两种方法，可以在不同的情况下使用：单周期解决方案和顺序时间段提供。我们在离线模式下使用反事实评估来评估这些方法与竞争方法。我们还讨论了可能影响约束优化在线性能的三个实际方面：容量确定、流量到达模式和大规模环境下的聚类。
Businesses, such as Amazon, department store chains, home furnishing store chains, Uber, and Lyft, frequently offer deals, product discounts and incentives to drive sales, increase new product acceptance and engage with users. In order to appeal to diverse user groups, these businesses typically design more than one promotion offer but market different ones to different users. For instance, Uber offers a percentage discount in the rides to some users and a low fixed price to others. In this paper, we propose solutions to optimally recommend promotions and items to maximize user conversion constrained by user eligibility and item or offer capacity (limited quantity of items or offers) simultaneously. We achieve this through an offer recommendation model based on Min-Cost Flow network optimization, which enables us to satisfy the constraints within the optimization itself and solve it in polynomial time. We present two approaches that can be used in various settings: single period solution and sequential time period offering. We evaluate these approaches against competing methods using counterfactual evaluation in offline mode. We also discuss three practical aspects that may affect the online performance of constrained optimization: capacity determination, traffic arrival pattern and clustering for large scale setting.