MIRROR: A Multi-View Reciprocal Recommender System for Online Recruitment

Reciprocal Recommender Systems (RRSs) which aim to satisfy the preferences of both service providers and seekers simultaneously has attracted significant research interest in recent years. Existing studies on RRSs mainly focus on modeling the bilateral interactions between the users on both sides to capture the user preferences. However, due to the presence of exposure bias, modeling user preferences solely based on bilateral interactions often lacks precision. Additionally, in RRSs, users may exhibit varying preferences when acting in different roles, and how to effectively model users from multiple perspectives remains a substantial problem. To solve the above challenges, in this paper, we propose a novel MultI-view Reciprocal Recommender system for Online Recruitment (MIRROR). Specifically, we first propose to model the users from three different views, respectively search, active, and passive views, and we further design several Transformer-based sequential models to capture the user representation corresponding to each view. Then, we propose to divide the bilateral matching process into three stages, respectively apply, reply, and match, and a multi-stage output layer is designed based on the above multi-view modeling results. To train our MIRROR model, we first design a multi-task learning loss based on the multi-stage output results. Moreover, to bridge the semantic gap between search queries and user behaviors, we additionally design a supplementary task for next-query prediction. Finally, we conduct both offline experiments on five real-world datasets and online A/B tests, and the experiment results clearly validate the effectiveness of our MIRROR model compared with several state-of-the-art baseline methods.