Bilateral Multi-Behavior Modeling for Reciprocal Recommendation in Online Recruitment

Recent years have witnessed the rapid development of online recruitment platforms, which provide a convenient way for matching job seekers and recruiters by leveraging recommendation systems. Indeed, this is a
reciprocal recommendation
problem which needs to consider the preferences of both job seekers and recruiters simultaneously, making it different from traditional uni-directional user-item recommendation problems. Existing studies mainly focus on building recommendation models based on the matched person-job pairs via text matching or collaborative filtering methods. However, we propose that these methods are limited and insufficient for user modeling in recruitment platforms, since the abundant multi-typed bilateral behaviors (e.g.,
apply for conversation
and
neglect the candidates
) among users have been largely ignored. Therefore, in this paper, we propose a novel BilAteral Multi-BehaviOr mOdeling (BAMBOO) method for reciprocal recommendation in online recruitment, which can model the multi-typed interactions between job seekers and recruiters from two different perspectives, respectively
expectation perspective
and
competitiveness perspective
. Specifically, for the expectation perspective, we propose to format the historical behaviors of different users as bilateral multi-behavior sequences, and we utilize a transformer-based model to learn the representations of what the users
want
to obtain. For the competitiveness perspective, we propose to construct a bilateral interaction heterogeneous graph to describe the entire recruitment market, and further utilize a heterogeneous graph transformer-based model to learn the representations of what the users
can
obtain. Moreover, we utilize contrastive learning methods to enhance these two modules. Furthermore, we propose to decompose the matching probability between job seekers and recruiters into the product of two parts, respectively the probability of the active party initiating the conversation and the probability of the passive party accepting it, and we train our model based on a multi-task learning strategy. Finally, we conduct both offline experiments on real-world datasets and online A/B test, and the experiment results validate the effectiveness of our BAMBOO model compared with several state-of-the-art baseline methods.