On E-commerce stores, 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.

翻译：在电商平台上，存在丰富的推荐内容以帮助购物者更高效地购物。然而，面对海量商品，选择最相关的内容以减轻信息过载的负担至关重要。我们引入了一种由线性因果赌博机算法驱动的内容排名服务，用于在每个情境下为每位购物者对内容进行排序和选择。该算法主要利用聚合的客户行为特征，而忽略单个购物者层面的过往活动。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、客户潜在购物目标以及客户与内容类别之间的关联性。该模型生成了更个性化的内容排名，其nDCG指标提升了12.08%。