Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's request. In such scenarios, a few items receive a majority of exposures, while many items receive very few. This imbalance leads to poorer experiences and decreased activity among the creators receiving less feedback, harming the recommender system in the long term. To this end, we develop a creator-side recommender system, called DualRec, to answer the following question: how to find the most suitable users for each item to enhance the creators' experience? We show that typical user-side recommendation algorithms, such as retrieval and ranking algorithms, can be adapted into the creator-side versions with just a few modifications. This greatly simplifies algorithm design in DualRec. Moreover, we discuss a unique challenge in DualRec: the user availability issue, which is not present in user-side recommender systems. To tackle this issue, we incorporate a user availability calculation (UAC) module to effectively enhance DualRec's performance. DualRec has already been implemented in Kwai, a short video recommendation system with over 100 millions user and over 10 million creators, significantly improving the experience for creators.

翻译：用户与创作者是推荐系统的两大核心组成部分。典型的推荐系统侧重于用户端，根据每位用户的请求提供最合适的项目。在此类场景中，少数项目获得了大部分曝光，而多数项目仅获得极少曝光。这种不平衡导致获得较少反馈的创作者体验变差、活跃度下降，长远来看损害了推荐系统的健康发展。为此，我们开发了一种名为DualRec的创作者端推荐系统，以回答以下问题：如何为每个项目寻找最合适的用户以提升创作者体验？我们证明，典型的用户端推荐算法（如检索与排序算法）仅需少量修改即可适配为创作者端版本，这极大简化了DualRec的算法设计。此外，我们探讨了DualRec中特有的挑战：用户可用性问题，该问题在用户端推荐系统中并不存在。为解决此问题，我们引入了用户可用性计算模块，有效提升了DualRec的性能。DualRec已在拥有超1亿用户与超1000万创作者的短视频推荐系统Kwai中部署实施，显著改善了创作者的体验。