The confluence of Search and Recommendation services is a vital aspect of online content platforms like Kuaishou and TikTok. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within the academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in this field. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 300 million daily active users. Previous research in this field has predominantly employed publicly available datasets that are semi-synthetic and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR records genuine user behaviors, the occurrence of each interaction within either search or recommendation service, and the users' transitions between the two services. This work aids in joint modeling of S&R, and the utilization of search data for recommenders (and recommendation data for search engines). Additionally, due to the diverse feedback labels of user-video interactions, KuaiSAR also supports a wide range of other tasks, including intent recommendation, multi-task learning, and long sequential multi-behavior modeling etc. We believe this dataset will facilitate innovative research and enrich our understanding of S&R services integration in real-world applications.

翻译：搜索与推荐服务的融合是快手、TikTok等在线内容平台的关键组成部分。业界从业者普遍采用搜索与推荐联合建模这一直观方法。然而，学术界在该领域的研究明显不足，主要由于缺乏公开可用的数据集。因此，学术界与工业界在该领域的研究工作之间出现了显著差距。为弥补这一差距，我们首次引入大规模真实世界数据集KuaiSAR，该数据集整合了来自中国领先短视频应用快手（日活跃用户超3亿）的搜索与推荐行为。此前该领域的研究主要采用半合成模拟的公开数据集，包含人工构造的搜索行为。与以往数据集不同，KuaiSAR记录了用户的真实行为、每次交互发生在搜索或推荐服务中的具体情况，以及用户在这两种服务之间的转换。该工作有助于搜索与推荐的联合建模，以及利用搜索数据优化推荐系统（以及利用推荐数据优化搜索引擎）。此外，由于用户-视频交互具备多样化的反馈标签，KuaiSAR还支持广泛的其他任务，包括意图推荐、多任务学习、长序列多行为建模等。我们相信，该数据集将促进创新研究，并加深对真实应用中搜索与推荐服务融合的理解。