Collaborative software development platforms (such as GitHub and GitLab) have become increasingly popu- lar as they have attracted thousands of external contributors to open source projects. External contributors may submit their contributions via pull requests (PRs), which must be reviewed before being integrated into the central repository. During the review process, reviewers provide feedback to contributors, conduct tests and request further modifications before finally accepting or rejecting the contributions. The role of reviewers is key to maintain the effective review process of the project. However, the number of decisions that reviewers can take is far superseded by the increasing number of pull requests submissions. Reviewers need help to perform more decisions on pull requests within their limited working time. In this paper [2], we propose a learning-to-rank (LtR) approach to recommend pull requests that can be quickly reviewed by reviewers. Different from a binary model for predicting the decisions of pull requests, our ranking approach complements the existing list of pull requests based on their likelihood of being quickly merged or rejected. We use 18 metrics to build LtR models and we use six different LtR algorithms, such as ListNet, RankNet, MART and random forest. We conduct empirical studies on 74 Java projects to compare the performances of the six LtR algorithms.