  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/.
