Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag-of-items), implicitly assuming that interactions separated by minutes are as semantically related as those separated by months. This simplification flattens the rich temporal structure of user behavior, obscuring the distinction between coherent consumption sessions and gradual interest drifts. In this work, we introduce TAI2Vec (Time-Aware Item-to-Vector), a family of lightweight embedding models that integrates temporal proximity directly into the representation learning process. Unlike approaches that apply global time constraints, TAI2Vec is user-adaptive, tailoring its temporal definitions to individual interaction paces. We propose two complementary strategies: TAI2Vec-Disc, which utilizes personalized anomaly detection to dynamically segment interactions into semantic sessions, and TAI2Vec-Cont, which employs continuous, user-specific decay functions to weigh item relationships based on their relative temporal distance. Experimental results across eight diverse datasets demonstrate that TAI2Vec consistently produces more accurate and behaviorally grounded representations than static baselines, achieving competitive or superior performance in over 80% of the datasets, with improvements of up to 135%. The source code is publicly available at https://github.com/UFSCar-LaSID/tai2vec.

翻译：有效的用户建模需要区分短期和长期的偏好演变。虽然物品嵌入已成为推荐系统的关键组成部分，但像Item2Vec这样的标准方法将用户历史视为无序集合（词袋模型），隐含地假设间隔几分钟的交互与间隔数月的交互具有同等的语义相关性。这种简化手段扁平化了用户行为丰富的时态结构，模糊了连贯的消费会话与渐进的兴趣漂移之间的区别。在这项工作中，我们引入了TAI2Vec（时间感知物品到向量），这是一系列轻量级嵌入模型，它将时间邻近性直接融入表示学习过程。与采用全局时间约束的方法不同，TAI2Vec具有用户自适应能力，能够根据个体交互节奏定制其时态定义。我们提出了两种互补策略：TAI2Vec-Disc，通过个性化异常检测动态将交互分割为语义会话；以及TAI2Vec-Cont，利用用户特定的连续衰减函数，根据相对时间距离对物品关系进行加权。在八个不同数据集上的实验结果表明，TAI2Vec始终能够生成比静态基线更准确且基于行为的表征，在超过80%的数据集上达到竞争性或更优的性能，改进幅度高达135%。源代码已在 https://github.com/UFSCar-LaSID/tai2vec 公开提供。