论文标题
Keen2act：在线社交协作平台中的活动建议
Keen2Act: Activity Recommendation in Online Social Collaborative Platforms
论文作者
论文摘要
Github和Stack Overflow等社会协作平台已越来越多地通过协作努力来提高工作生产力。为了改善这些平台中的用户体验，希望拥有一个建议系统，该系统不仅可以向用户建议项目（例如GitHub存储库），而且还可以在建议的项目（例如，分配存储库）上执行的活动。为此，我们提出了一种称为Keen2Act的新方法，该方法将推荐问题分解为两个阶段：敏锐的ACT步骤。敏锐的步骤标识了给定用户，他/她可能会感兴趣的一组（子）项目。然后，ACT步骤向用户建议在确定的项目集上执行的活动。这种分解提供了一种实用方法来解决复杂的活动建议任务，同时产生更高的建议质量。我们使用两个现实世界数据集评估了我们提出的方法，并获得有希望的结果，从而使Keen2act优于几个基线模型。
Social collaborative platforms such as GitHub and Stack Overflow have been increasingly used to improve work productivity via collaborative efforts. To improve user experiences in these platforms, it is desirable to have a recommender system that can suggest not only items (e.g., a GitHub repository) to a user, but also activities to be performed on the suggested items (e.g., forking a repository). To this end, we propose a new approach dubbed Keen2Act, which decomposes the recommendation problem into two stages: the Keen and Act steps. The Keen step identifies, for a given user, a (sub)set of items in which he/she is likely to be interested. The Act step then recommends to the user which activities to perform on the identified set of items. This decomposition provides a practical approach to tackling complex activity recommendation tasks while producing higher recommendation quality. We evaluate our proposed approach using two real-world datasets and obtain promising results whereby Keen2Act outperforms several baseline models.
