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，嵌入向量常被用于编码用户行为与物品特征，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）用户嵌入向量可能限制所捕获兴趣的多样性；2）为保持用户嵌入的实时更新需要构建昂贵的实时基础设施。本文提出一种实用的工业级方法以突破这些瓶颈。该方法通过动态更新用户画像，并利用预计算的嵌入向量及其相似度，每两分钟完成一次信息流组合。我们在荷兰与比利时最大电商平台Bol上完成该方法的测试与部署，用于个性化促销商品推荐。实验表明，该方法显著提升了用户参与度与体验，带来4.9%的转化率提升。