Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender’s performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one \emph{Pull-Request (PR)} (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one~\emph{PR} with multiple reviewers herein) and developed a new recommender, namely~\emph{HGRec}, which is evaluated by 12 OSS projects with more than 87K \emph{PR}s, 680K comments in terms of~\emph{accuracy} and~\emph{recommendation distribution}. The results indicate that~\emph{HGRec} outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders,~\emph{HGRec} is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers’ workload congestion issue. Moreover, since~\emph{HGRec} is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.