KuaiSAR: A Unified Search And Recommendation Dataset

<p>The confluence of Search and Recommendation (S&amp;R) services is a vital aspect of online content platforms like Kuaishou and TikTok. The integration of S&amp;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,&nbsp;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&rsquo; transitions between the two services. This work aids in joint modeling of S&amp;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&amp;R services integration in real-world applications.</p>