kuairec — Kuaishou short video views, fully observed (KuaiRec, 2020)

Description

Networks of the users of the short video sharing app Kuaishou (快手), the videos they watched, and their friendships, from the KuaiRec dataset, collected by C. Gao and colleagues (University of Science and Technology of China, Kuaishou and the National University of Singapore) from the recommendation logs of the app between 22 June and 10 September 2020. Its distinguishing feature is a "fully observed" part: for a set of 1,411 users and 3,327 videos, the videos were shown to all the users, so that the users' reactions to almost all of the videos are known (99.6%; the missing ones are videos of authors that the users had blocked).

There are three networks:

big: the views of 7,176 users and 10,728 videos (the "big matrix" of the source, 12.5 million views, of which 2.2 million repeat an earlier view of the same video by the same user). A directed edge from a user to a video is a view of the video by the user. The network is bipartite and a multigraph.
small: the views of the fully observed part (the "small matrix", 1,411 users and 3,327 videos, 4.7 million views), in the same form, with only these users and videos. Its views are not part of big (the two sets of views are disjoint, although the users and videos of small also appear in big). The time of 181,992 of these views is missing.
social: the friendships among the 7,176 users (an undirected edge between two users who are friends). Only 472 users have friends in the data.

The node properties of the users come from the user features of the source (including the raw features released in January 2026: gender, age range, phone, location, installed apps, ...). Those of the videos come from the daily video features (the values of the last day, for the few that change over time: visibility, size and tag), and from the captions and categories released in June 2024. The daily statistics of the videos (counts of plays, likes, comments, ... per day), the binned versions of the counts of the users, and the multi-level category annotations with probabilities (video_raw_categories_multi.csv) are left out. The user and video identifiers are those of the source (re-indexed by it).

The networks have the following properties:

(Node) is_user (big, small): whether the node is a user (otherwise it is a video).
(Node) id: the identifier of the user or video in the dataset.
(Node) The user properties below are as given by the source (UNKNOWN, or -1 for the counts and flags, if unknown), and empty, or -1, for videos.
(Node) user_active_degree (users): the activity level, e.g. full_active, high_active.
(Node) is_lowactive_period, is_live_author, is_photo_author, follow_user_num, fans_user_num, friend_user_num, gender, age_range, phone_brand, phone_model, platform, os_version, app_download_channel, app_version (users): the corresponding attributes of the user.
(Node) register_days (users): days since registration.
(Node) mod_price (users): the price of the phone model.
(Node) fre_country, fre_country_region, fre_province, fre_city, fre_city_level, fre_community_type (users): the most frequent location of the user.
(Node) is_install_douyin, is_install_huoshan, is_install_xigua, is_install_douyu, is_install_huya, is_install_yy (users): whether other video apps are installed.
(Node) isp (users): the internet service provider.
(Node) onehot_features (users): the 18 anonymized categorical features onehot_feat0 to onehot_feat17.
(Node) The video properties below (in big and small) are empty, -1 or NaN for users.
(Node) author_id (videos): the id of the author of the video.
(Node) video_type (videos): NORMAL or AD.
(Node) upload_time (videos): the upload date, as Unix time.
(Node) upload_type (videos): e.g. ShortImport.
(Node) visible_status (videos): public, private or only friends.
(Node) duration (videos): in milliseconds.
(Node) width, height (videos): in pixels.
(Node) music_id, video_tag_id (videos): the ids of the music and of the tag of the video.
(Node) video_tag_name (videos): the tag of the video, in Chinese.
(Node) tags (videos): the ids of the categories of the video, comma-separated.
(Node) caption (videos): the caption of the video.
(Node) manual_cover_text (videos): the text on the cover of the video.
(Node) topic_tag (videos): the topic hashtags, as a list.
(Node) first_level_category, second_level_category, third_level_category (videos): the names of the categories of the video at three levels, in Chinese, UNKNOWN if none.
(Edge) time (big, small): the time of the view, as Unix time (with milliseconds; NaN if missing).
(Edge) play_duration (big, small): how long the user watched the video, in milliseconds.
(Edge) watch_ratio (big, small): the ratio of the time watched to the duration of the video (it can exceed 1, if the video was replayed).

Tags

Social Online Preferences Bipartite Multigraph Timestamps Metadata

Added

2026-10-28

Citation

C. Gao, S. Li, W. Lei, J. Chen, B. Li, P. Jiang, X. He, J. Mao, T.-S. Chua, "KuaiRec: A Fully-Observed Dataset and Insights for Evaluating Recommender Systems." Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 540-550 (2022) 10.1145/3511808.3557220 Sci-Hub

Upstream URL OK

https://kuairec.com/
https://doi.org/10.5281/zenodo.18164998

Upstream license

CC BY-SA 4.0, https://github.com/chongminggao/KuaiRec/blob/a22f13b07d8b9d6b15e0cec2e97393f2bb0c40b3/LICENSE

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
big	17,904	12,530,806	699.89	1417.10	2206.84	1.49	-0.05	0.00	5	1.00	Directed	Bipartite	is_user id user_active_degree gender age_range phone_brand phone_model mod_price fre_country fre_country_region fre_province fre_city fre_city_level fre_community_type platform os_version app_download_channel app_version isp is_lowactive_period is_live_author is_photo_author follow_user_num fans_user_num friend_user_num register_days is_install_douyin is_install_huoshan is_install_xigua is_install_douyu is_install_huya is_install_yy onehot_features author_id music_id video_tag_id duration width height video_type upload_type visible_status video_tag_name upload_time tags caption manual_cover_text topic_tag first_level_category second_level_category third_level_category	time play_duration watch_ratio	163.5 MiB	229.8 MiB	229.1 MiB	228.0 MiB
small	4,738	4,676,570	987.03	872.95	2157.47	0.23	-1.00	0.00	3	1.00	Directed	Bipartite	is_user id user_active_degree gender age_range phone_brand phone_model mod_price fre_country fre_country_region fre_province fre_city fre_city_level fre_community_type platform os_version app_download_channel app_version isp is_lowactive_period is_live_author is_photo_author follow_user_num fans_user_num friend_user_num register_days is_install_douyin is_install_huoshan is_install_xigua is_install_douyu is_install_huya is_install_yy onehot_features author_id music_id video_tag_id duration width height video_type upload_type visible_status video_tag_name upload_time tags caption manual_cover_text topic_tag first_level_category second_level_category third_level_category	time play_duration watch_ratio	63.0 MiB	89.2 MiB	87.9 MiB	89.1 MiB
social	7,176	335	0.09	0.41	4.00	1.40	0.61	0.65	3	0.00	Undirected	Unipartite	id user_active_degree gender age_range phone_brand phone_model mod_price fre_country fre_country_region fre_province fre_city fre_city_level fre_community_type platform os_version app_download_channel app_version isp is_lowactive_period is_live_author is_photo_author follow_user_num fans_user_num friend_user_num register_days is_install_douyin is_install_huoshan is_install_xigua is_install_douyu is_install_huya is_install_yy onehot_features		303 KiB	395 KiB	382 KiB	489 KiB