Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences. Conventional recommender systems (RS) suggest relevant but time-invariant items to users by accounting for their past purchases. These recommendations only map to customers' general preferences rather than a customer's specific needs immediately preceding a purchase. In contrast, RSs that consider the order of transactions, purchases, or experiences to measure evolving preferences can offer more salient and effective recommendations to customers: Sequential RSs not only benefit from a better behavioral understanding of a user's current needs but also better predictive power. In this paper, we demonstrate and rank the effectiveness of a sequential recommendation system by utilizing a production dataset of over 2.7 million credit card transactions for 46K cardholders. The method first employs an autoencoder on raw transaction data and submits observed transaction encodings to a GRU-based sequential model. The sequential model produces a MAP@1 metric of 47% on the out-of-sample test set, in line with existing research. We also discuss implications for embedding real-time predictions using the sequential RS into Nexus, a scalable, low-latency, event-based digital experience architecture.

翻译：在提供当代数字营销体验时，推荐的时效性和上下文准确性日益重要。传统推荐系统通过考虑用户的历史购买行为，向用户推荐相关但时间不变的项目。这些推荐仅映射用户的总体偏好，而非用户购买前的具体即时需求。相比之下，考虑交易、购买或体验顺序以衡量用户偏好演变的推荐系统，能够向用户提供更贴切、更有效的推荐：序列推荐系统不仅受益于对用户当前需求的更优行为理解，还具有更好的预测能力。本文利用包含46K持卡人、超过270万信用卡交易的生产数据集，论证并排序了序列推荐系统的有效性。该方法首先对原始交易数据采用自编码器，并将观测到的交易编码输入基于门控循环单元（GRU）的序列模型。该序列模型在样本外测试集上达到47%的MAP@1指标，与现有研究一致。我们还讨论了将基于序列推荐系统的实时预测嵌入Nexus（一种可扩展、低延迟、基于事件的数字体验架构）的相关启示。