Sequential recommender systems identify user preferences from their past interactions to predict subsequent items optimally. Although traditional deep-learning-based models and modern transformer-based models in previous studies capture unidirectional and bidirectional patterns within user-item interactions, the importance of temporal contexts, such as individual behavioral and societal trend patterns, remains underexplored. Notably, recent models often neglect similarities in users' actions that occur implicitly among users during analogous timeframes-a concept we term vertical temporal proximity. These models primarily adapt the self-attention mechanisms of the transformer to consider the temporal context in individual user actions. Meanwhile, this adaptation still remains limited in considering the horizontal temporal proximity within item interactions, like distinguishing between subsequent item purchases within a week versus a month. To address these gaps, we propose a sequential recommendation model called TemProxRec, which includes contrastive learning and self-attention methods to consider temporal proximities both across and within user-item interactions. The proposed contrastive learning method learns representations of items selected in close temporal periods across different users to be close. Simultaneously, the proposed self-attention mechanism encodes temporal and positional contexts in a user sequence using both absolute and relative embeddings. This way, our TemProxRec accurately predicts the relevant items based on the user-item interactions within a specific timeframe. We validate this work through comprehensive experiments on TemProxRec, consistently outperforming existing models on benchmark datasets as well as showing the significance of considering the vertical and horizontal temporal proximities into sequential recommendation.

翻译：序列推荐系统通过分析用户的历史交互行为来识别其偏好，从而最优地预测后续物品。尽管先前研究中传统的基于深度学习的模型和基于Transformer的现代模型能够捕获用户-物品交互中的单向和双向模式，但时间上下文（如个体行为模式和社会趋势模式）的重要性仍未得到充分探索。值得注意的是，近期模型常忽略用户在相似时间框架内隐含发生的行动相似性——我们将其定义为垂直时间邻近性。这些模型主要借鉴Transformer的自注意力机制来考虑个体用户行动中的时间上下文，但在处理物品交互中的水平时间邻近性（例如区分一周内与一个月内的后续商品购买行为）时仍存在局限性。为弥补这些不足，我们提出名为TemProxRec的序列推荐模型，该模型融合对比学习与自注意力方法，综合考虑用户-物品交互跨域与域内的时间邻近性。所提出的对比学习方法通过学习不同用户在相近时间段内选择物品的表示，使其趋于接近；同时，提出的自注意力机制通过绝对嵌入与相对嵌入对用户序列中的时间与位置上下文进行编码。通过这种方式，我们的TemProxRec能够基于特定时间框架内的用户-物品交互准确预测相关物品。通过全面的实验验证，TemProxRec在基准数据集上持续超越现有模型，同时证明了将垂直与水平时间邻近性纳入序列推荐的重要性。