Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{collaborative signals} from behaviors does not fully grasp user interests. It is also significant to model the \textit{semantic relatedness} reflected in content features, e.g., images and text. Towards that end, in this paper, we aim to enhance the SRS tasks by effectively unifying collaborative signals and semantic relatedness together. Notably, we empirically point out that it is nontrivial to achieve such a goal due to semantic gap issues. Thus, we propose an end-to-end two-stream architecture for sequential recommendation, named TSSR, to learn user preferences from ID-based and content-based sequence. Specifically, we first present novel hierarchical contrasting module, including coarse user-grained and fine item-grained terms, to align the representations of inter-modality. Furthermore, we also design a two-stream architecture to learn the dependence of intra-modality sequence and the complex interactions of inter-modality sequence, which can yield more expressive capacity in understanding user interests. We conduct extensive experiments on five public datasets. The experimental results show that the TSSR could yield superior performance than competitive baselines. We also make our experimental codes publicly available at https://anonymous.4open.science/r/TSSR-2A27/.

翻译：序列推荐系统（SRS）通过建模按时间排序的历史行为，能够捕捉用户动态偏好。尽管有效，但仅关注行为中的协同信号无法完全把握用户兴趣，而建模内容特征（如图像和文本）中蕴含的语义关联同样重要。为此，本文旨在通过有效融合协同信号与语义关联来提升序列推荐任务性能。值得注意的是，我们通过实验指出，由于语义鸿沟问题，实现该目标并非易事。因此，我们提出一种用于序列推荐的端到端双流架构TSSR，从基于ID和基于内容的序列中学习用户偏好。具体而言，我们首先提出包含粗粒度用户级和细粒度物品级项的新型层次对比模块，用于对齐跨模态表示。此外，我们设计了双流架构以学习模态内序列依赖性与模态间序列的复杂交互，从而增强对用户兴趣的理解能力。我们在五个公开数据集上进行了大量实验，结果表明TSSR能够取得优于竞争基线的性能。我们已在https://anonymous.4open.science/r/TSSR-2A27/ 公开实验代码。