Transformers emerged as powerful methods for sequential recommendation. However, existing architectures often overlook the complex dependencies between user preferences and the temporal context. In this short paper, we introduce MOJITO, an improved Transformer sequential recommender system that addresses this limitation. MOJITO leverages Gaussian mixtures of attention-based temporal context and item embedding representations for sequential modeling. Such an approach permits to accurately predict which items should be recommended next to users depending on past actions and the temporal context. We demonstrate the relevance of our approach, by empirically outperforming existing Transformers for sequential recommendation on several real-world datasets.

翻译：Transformer已成为序列推荐领域的强力方法。然而，现有架构往往忽略了用户偏好与时间上下文之间的复杂依赖关系。本文提出MOJITO——一种改进的Transformer序列推荐系统，旨在解决这一局限。MOJITO利用基于注意力时间上下文与物品嵌入表示的高斯混合机制进行序列建模。该方法能够根据用户历史行为与时间上下文，精准预测应推荐给用户的下一项物品。通过在多个真实数据集上的实证研究，我们验证了该方法在序列推荐性能上显著优于现有Transformer模型。