Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties. Although dynamic user modeling has been well-studied in sequential recommender systems, existing solutions are developed in a user-oriented manner. Therefore, it is non-trivial to adapt sequential recommendation algorithms to reciprocal recommendation. In this paper, we formulate RRS as a distinctive sequence matching task, and further propose a new approach ReSeq for RRS, which is short for Reciprocal Sequential recommendation. To capture dual-perspective matching, we propose to learn fine-grained sequence similarities by co-attention mechanism across different time steps. Further, to improve the inference efficiency, we introduce the self-distillation technique to distill knowledge from the fine-grained matching module into the more efficient student module. In the deployment stage, only the efficient student module is used, greatly speeding up the similarity computation. Extensive experiments on five real-world datasets from two scenarios demonstrate the effectiveness and efficiency of the proposed method. Our code is available at https://github.com/RUCAIBox/ReSeq/.

翻译：互惠推荐系统（RRS）考虑双方的双向匹配，已广泛应用于在线约会和招聘等在线平台。现有RRS模型主要捕捉静态用户偏好，忽略了用户偏好的演化以及双方之间动态的匹配关系。尽管序列推荐系统中已对动态用户建模进行了深入研究，但现有解决方案是以用户为中心开发的。因此，将序列推荐算法适配到互惠推荐场景并非易事。本文将RRS表述为一种独特的序列匹配任务，并进一步提出了一种针对RRS的新方法ReSeq（即互惠序列推荐的缩写）。为捕捉双视角匹配，我们提出通过跨不同时间步的协同注意力机制学习细粒度序列相似性。此外，为提升推理效率，我们引入自蒸馏技术，将细粒度匹配模块中的知识蒸馏到更高效的学生模块中。在部署阶段仅使用高效的学生模块，大幅加速相似性计算。在两个场景下的五个真实数据集上的大量实验证明了所提方法的有效性和效率。我们的代码可在 https://github.com/RUCAIBox/ReSeq/ 获取。