User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven temporal distribution of user interactions highlights the evolving patterns of interests, making it challenging to accurately capture shifts in interests using comprehensive historical behaviors. To address this, we propose SLSRec, a novel Session-based model with the fusion of Long- and Short-term Recommendations that effectively captures the temporal dynamics of user interests by segmenting historical behaviors over time. Unlike conventional models that combine long- and short-term user interests into a single representation, compromising recommendation accuracy, SLSRec utilizes a self-supervised learning framework to disentangle these two types of interests. A contrastive learning strategy is introduced to ensure accurate calibration of long- and short-term interest representations. Additionally, an attention-based fusion network is designed to adaptively aggregate interest representations, optimizing their integration to enhance recommendation performance. Extensive experiments on three public benchmark datasets demonstrate that SLSRec consistently outperforms state-of-the-art models while exhibiting superior robustness across various scenarios.We will release all source code upon acceptance.

翻译：用户兴趣通常涵盖长期偏好和短期意图，反映了用户行为在不同时间维度上的动态特性。用户交互行为在时间分布上的不均匀性凸显了兴趣的演化模式，使得通过完整历史行为准确捕捉兴趣变迁变得极具挑战。为解决这一问题，我们提出SLSRec——一种融合长短期推荐的新型会话模型，通过分段处理时间维度的历史行为，有效捕捉用户兴趣的时间动态特性。不同于传统模型将长短期用户兴趣合并为单一表示而损害推荐精度，SLSRec采用自监督学习框架将这两类兴趣进行解耦。我们引入对比学习策略以确保长、短期兴趣表示的精准校准。此外，设计了一种基于注意力的融合网络，自适应聚合兴趣表示以优化整合效果，从而提升推荐性能。在三个公开基准数据集上的大量实验表明，SLSRec在各类场景中持续优于现有最优模型，并展现出卓越的鲁棒性。论文被接收后我们将公开全部源代码。