On E-commerce stores (Amazon, eBay etc.) there are rich recommendation content to help shoppers shopping more efficiently. However given numerous products, it's crucial to select most relevant content to reduce the burden of information overload. We introduced a content ranking service powered by a linear causal bandit algorithm to rank and select content for each shopper under each context. The algorithm mainly leverages aggregated customer behavior features, and ignores single shopper level past activities. We study the problem of inferring shoppers interest from historical activities. We propose a deep learning based bandit algorithm that incorporates historical shopping behavior, customer latent shopping goals, and the correlation between customers and content categories. This model produces more personalized content ranking measured by 12.08% nDCG lift. In the online A/B test setting, the model improved 0.02% annualized commercial impact measured by our business metric, validating its effectiveness.

翻译：在电子商务平台（如亚马逊、eBay等）上存在丰富的推荐内容以帮助购物者更高效地完成购物。然而，面对海量商品，筛选最相关的内容以减轻信息过载负担至关重要。我们提出了一种基于线性因果赌博机算法的内容排序服务，用于在特定上下文情境下为每位购物者排序和选择内容。该算法主要依赖聚合的顾客行为特征，而忽略单个购物者层面的历史活动。我们研究了从历史活动中推断购物者兴趣的问题，并提出了一种基于深度学习的赌博机算法，该算法融合了历史购物行为、购物者潜在购物目标以及顾客与内容类别之间的关联性。该模型生成的个性化内容排序在nDCG指标上提升了12.08%。在线A/B测试中，该模型在业务指标衡量的年化商业影响上提升了0.02%，验证了其有效性。