Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and virtually every industry where personalisation facilitates better user experience or boosts sales and customer engagement. The main goal of these systems is to analyse past user behaviour to predict which items are of most interest to users. They are typically built with the use of matrix-completion techniques such as collaborative filtering or matrix factorisation. However, although these approaches have achieved tremendous success in numerous real-world applications, their effectiveness is still limited when users might interact multiple times with the same items, or when user preferences change over time. We were inspired by the approach that Natural Language Processing techniques take to compress, process, and analyse sequences of text. We designed a recommender system that induces the temporal dimension in the task of item recommendation and considers sequences of item interactions for each user in order to make recommendations. This method is empirically shown to give highly accurate predictions of user-items interactions for all users in a retail environment, without explicit feedback, besides increasing total sales by 5% and individual customer expenditure by over 50% in an A/B live test.

翻译：推荐系统是机器学习与数据科学最成功的应用之一，在电子商务、流媒体内容、电子邮件营销以及几乎所有通过个性化提升用户体验或促进销售与客户参与的行业中都取得了巨大成功。这类系统的主要目标是分析用户历史行为，以预测哪些物品最令用户感兴趣。它们通常采用矩阵补全技术（如协同过滤或矩阵分解）构建。然而，尽管这些方法在众多实际应用中取得了显著成就，但当用户可能多次与同一物品交互，或用户偏好随时间变化时，其有效性仍受到限制。受自然语言处理技术压缩、处理和分析文本序列方法的启发，我们设计了一种推荐系统，该模型在物品推荐任务中引入时间维度，通过分析每个用户的物品交互序列进行推荐。实验表明，该方法在零售环境下，无需显式反馈即可对所有用户提供高度准确的用户-物品交互预测，并且在A/B实时测试中使总销售额提升5%，单个客户消费额提升超过50%。