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.

翻译：在个性化推荐系统中，嵌入常被用于编码用户行为与物品，并通过近似最近邻搜索在嵌入空间中进行检索。然而，该方法面临两大挑战：1）用户嵌入可能限制所捕获兴趣的多样性；2）为保持嵌入的实时更新需要部署昂贵的实时计算基础设施。本文提出一种可在工业实践中克服上述挑战的方法。该方法通过动态更新客户画像并每两分钟生成一次信息流，利用预计算嵌入及其相似性实现推荐。我们在荷兰与比利时最大的电商平台之一Bol上测试并部署了该方法，用于个性化促销商品推荐。实验表明，该方法显著提升了客户参与度与体验，使转化率提升4.9%。