In personalized recommender systems, embeddings are often used to encode
customer actions and items, and retrieval is then performed in the embedding
space using approximate nearest neighbor search. However, this approach can
lead to two challenges: 1) user embeddings can restrict the diversity of
interests captured and 2) the need to keep them up-to-date requires an
expensive, real-time infrastructure. In this paper, we propose a method that
overcomes these challenges in a practical, industrial setting. The method
dynamically updates customer profiles and composes a feed every two minutes,
employing precomputed embeddings and their respective similarities. We tested
and deployed this method to personalise promotional items at Bol, one of the
largest e-commerce platforms of the Netherlands and Belgium. The method
enhanced customer engagement and experience, leading to a significant 4.9%
uplift in conversions.

在个性化推荐系统中，使用嵌入来编码客户行为和物品，并通过近似最近邻搜索在嵌入空间中执行检索。然而，这种方法可能会面临两个挑战：用户嵌入可能限制了所捕捉的兴趣的多样性，而且需要保持它们的实时更新需要代价高昂的基础设施。在本文中，我们提出了一种在实际工业环境中克服这些挑战的方法。该方法动态更新客户配置文件，并每两分钟组合一个推荐列表，使用预计算的嵌入及其相似度。我们在荷兰和比利时最大的电子商务平台之一的 Bol 上测试并部署了这种方法。该方法提升了客户参与和体验，导致转化率显著提高了 4.9%。