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：序列推荐系统通过用户历史交互行为识别其偏好，以最优方式预测后续物品。尽管传统基于深度学习的模型及现代基于Transformer的模型在先前研究中已能捕捉用户-物品交互中的单向与双向模式，但时间上下文（如个体行为模式及社会趋势模式）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间在相似时间框架内隐含发生的动作相似性——我们将其定义为垂直时间近邻性。这些模型主要采用Transformer的自注意力机制来考虑个体用户动作的时间上下文，但在处理物品交互中的水平时间近邻性（如区分一周内与一个月内的后续物品购买行为）时仍存在局限。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，可同时兼顾用户-物品交互的跨维度与维度内时间近邻性。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所选物品的表征，使其表示趋于接近。同时，所设计的自注意力机制利用绝对嵌入与相对嵌入，对用户序列中的时间上下文和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-物品交互精准预测相关物品。我们通过全面实验对TemProxRec进行验证，结果表明该模型在基准数据集上持续超越现有模型，并证实了将垂直与水平时间近邻性纳入序列推荐的重要意义。