Modeling sequential interactions between users and items/products is crucial
in domains such as e-commerce, social networking, and education. Representation
learning presents an attractive opportunity to model the dynamic evolution of
users and items, where each user/item can be embedded in a Euclidean space and
its evolution can be modeled by an embedding t
用户嵌入在用户参与度预测和个性化服务中起着至关重要的作用。我们提出了 User Stateful Embedding (USE)，通过存储先前的模型状态并在未来重新访问，生成和反映用户的不断演变的行为，并通过与同一用户预测相结合的对比学习目标进一步提高嵌入的独特性和代表性。实验证明 USE 在动态用户建模中将历史和最近的用户行为序列整合到用户嵌入中具有卓越的性能。