In the sequential recommendation task, the recommender generally learns multiple embeddings from a user's historical behaviors, to catch the diverse interests of the user. Nevertheless, the existing approaches just extract each interest independently for the corresponding sub-sequence while ignoring the global correlation of the entire interaction sequence, which may fail to capture the user's inherent preference for the potential interests generalization and unavoidably make the recommended items homogeneous with the historical behaviors. In this paper, we propose a novel Dual-Scale Interest Extraction framework (DSIE) to precisely estimate the user's current interests. Specifically, DSIE explicitly models the user's inherent preference with contrastive learning by attending over his/her entire interaction sequence at the global scale and catches the user's diverse interests in a fine granularity at the local scale. Moreover, we develop a novel interest aggregation module to integrate the multi-interests according to the inherent preference to generate the user's current interests for the next-item prediction. Experiments conducted on three real-world benchmark datasets demonstrate that DSIE outperforms the state-of-the-art models in terms of recommendation preciseness and novelty.

翻译：在序列推荐任务中，推荐系统通常从用户的历史行为中学习多个嵌入表示，以捕捉用户的多样化兴趣。然而，现有方法仅针对相应子序列独立提取每个兴趣，忽略了整个交互序列的全局相关性，这可能导致无法捕捉用户对潜在兴趣泛化的内在偏好，并不可避免地使推荐项目与历史行为同质化。本文提出一种新颖的双尺度兴趣提取框架（DSIE），以精确估计用户当前兴趣。具体而言，DSIE通过全局尺度上关注用户整个交互序列的对比学习显式建模用户内在偏好，并在局部尺度上以细粒度捕捉用户多样化兴趣。此外，我们开发了一种新颖的兴趣聚合模块，根据内在偏好集成多兴趣，生成用户当前兴趣以预测下一项。在三个真实世界基准数据集上的实验表明，DSIE在推荐准确性和新颖性方面均优于现有最先进模型。