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KuaiSAR is a unified search and recommendation dataset containing the genuine user behavior logs collected from the short-video mobile app, Kuaishou (快手), a leading short-video app in China with over 300 million daily active users.
It is the first dataset which records genuine user behaviors, the occurrence of each interaction within either search or recommendation service, and the users’ transitions between the two services!


As shown in the following figure, Kuaishou provides both search and recommendation services. The figure illustrates integrated search and recommendation scenarios in Kuaishou app. When watching a video, the user can either scroll up and down to browse different videos with the recommendation service (from middle to left); or tap on the magnifying glass to access the search service (from middle to right).


From the user’s perspective, the boundary between search and recommendation services may not be distinct. Users experience a unified service that combines both search and recommendation functionalities. In the recommendation service, as illustrated in the figures below (a. and b.), there exist several designs that prompt users to transition into the search service. Similarly, in the search service, as shown in the figures below (c. and d.), various recommended queries are presented to stimulate users to engage in further searching.


The other two related datasets are: KuaiRec and KuaiRand.


Compared with other existing datasets, KuaiSAR has the following advantages:


Here we show some basic statistics. Check this page for more detailed Descriptions and Analytics.

KuaiSAR contains genuine search and recommendation behaviors of 25,877 users within a span of 19 days on the Kuaishou app. This dataset filters users based on a single condition: that users have used both search and recommendation services within the specified time period ranging from 2023/5.22 14:30 to 2023/6.10 9:30. As a result, the final dataset encompasses users with diverse levels of activity in either the search or recommendation services, thereby offering a comprehensive representation of users with varying degrees of engagement. Basic statistics of this dataset in the are summarized as follows:


Dataset #Users #Items #Queries #Actions
S-data 25,877 3,026,189 453,667 5,059,169
R-data 25,877 4,046,367 - 14,605,716
Total 25,877 6,890,707 453,667 19,664,885

where the ‘S’ and ‘R’ denote search and recommendation respectively.

In order to facilitate researchers to conduct experiments more quickly, we have also released a smaller version of the data. The difference is that KuaiSAR contains data from 2023/5/22 14:30 to 2023/6/10 9:30, while KuaiSAR-small only contains data from 2023/5/22 14:50 to 2023/5/31 14:50.


Dataset #Users #Items #Queries #Actions
S-data 25,877 2,012,476 267,608 3,171,231
R-data 25,877 2,281,034 7,493,101
Total 25,877 4,195,529 267,608 10,664,332

The short descriptions for each feature filed are listed as below. Please refer to this page for more details and examples.

Feature: Detailed Descriptions.
User&Item feature: Users and items have abundant side information. 5 (18) features for users (items).
S-action feature: S-actions have 9 features, e.g., search session IDs, query keywords, and sources of entering the search service.
R-action feature: R-actions has 12 features, including 9 types of user feedback, e.g., likes, follows, and entering search.
Social network: 576 users have friends.

Download the data:

KuaiSAR has been shared at


OPTION 1. Download via your browser:

You can download the dataset from this link.


The screenshot of the data download page:

OPTION 2: Download via the ‘wget’ command tool:

For the KuaiSAR dataset:



For the KuaiSAR-small dataset:





This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.



If you have any questions, please feel free to contact us through github issues or emails (,


If you find it helpful, please cite our paper ( LINK PDF ) or cite our website (

  title={KuaiSAR: A Unified Search And Recommendation Dataset},
  author={Zhongxiang Sun and Zihua Si and Xiaoxue Zang and Dewei Leng and Yanan Niu and Yang Song and Xiao Zhang and Jun Xu},
  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
  url = {},
  doi = {10.1145/3583780.3615123},