KuaiRand is an unbiased sequential recommendation dataset collected from the recommendation logs of the video-sharing mobile app, Kuaishou (快手). It is the first recommendation dataset with millions of intervened interactions of randomly exposed items inserted in the standard recommendation feeds!
Another related open-sourced dataset is KuaiRec.
The following figure gives an example of the dataset. It illustrates a user interaction sequence along with the user's rich feedback signals.
These feedback signals are collected from the two main user interfaces (UI) in Kuaishou APP shown as follows:
In the random exposure stage, each recommended video in the dataset has an equal probability being replaced by a random video sampled from an item pools. About
Compared with other datasets with random exposure, KuaiRand has the following advantages:
- ✅ It is the first sequential recommendation dataset with millions of intervened interactions of randomly exposed items inserted in the standard recommendation feeds.
- ✅ It has the most comprehensive side information including explicit user IDs, interaction timestamps, and rich features for users and items.
- ✅ It has 15 policies with each catered for a special recommendation scenario in Kuaishou App.
- ✅ We introduce 12 feedback signals (e.g., click, like, and view time) for each interaction to describe the user's comprehensive feedback.
- ✅ Each user has thousands of historical interactions on average.
- ✅ It has three versions to support various research directions in recommendation.
If you find it helpful, please cite our paper:
@inproceedings{gao2022kuairand,
title = {KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos},
author = {Gao, Chongming and Li, Shijun and Zhang, Yuan and Chen, Jiawei and Li, Biao and Lei, Wenqiang and Jiang, Peng and He, Xiangnan},
url = {https://doi.org/10.1145/3511808.3557624},
doi = {10.1145/3511808.3557624},
booktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management},
series = {CIKM '22},
location = {Atlanta, GA, USA},
numpages = {5},
year = {2022},
pages = {3953–3957}
}
2022.08.23: Cyberspace Administration of China (CAC) released a series of measures to control cross-border data transfer on July 7, 2022. The full text of the Measures for Security Assessment of Cross-border Data Transfer (《数据出境安全评估办法》) can be found here (currently available only in Mandarin Chinese and there are some English posts such as this). The Measures will take effect on September 1, 2022. Therefore, for now, we cannot provide this dataset to institutions outside China until CAC completes our assessment and gives us permission to share the data overseas. We sincerely apologize for the inconvenience.
2022.08.23: 国家互联网信息办公室于2022年7月7日出台了保护国家安全及个人隐私的《数据出境安全评估办法》，于2022年9月1日开始执行。其规定公司数据不能跨境自由流动。于是我们按照规定中止对国外机构提供该数据集，目前仅能将该数据提供给中国境内机构使用。在国家互联网信息办公室批准我们的数据出境请求后，我们将马上向所有人公开该数据。
2022.08.02: Our CIKM '22 paper is accepted.
2022.06.02: The paper introducing this dataset is submitted to CIKM '22 Resource Track.
Due to regulations of the Chinese government (as mentioned above), for now, we have to use email to make sure that you are a researcher or practitioner in China. Then you have to sign some files to promise that you will NOT disclose this dataset. Please send us your name, institution (which must lie in China) via this email address: [email protected]
We are very sorry for the inconvenience! We will try to overcome this problem soon.
根据国家最近出台的个人隐私信息保护法以及数据出境安全评估办法，我们目前只提供数据集给中国机构（大学、研究所、公司），且需要您将您的姓名、机构信息发给我方，我们将回复您相关的保密协议。在签署协议后，才能给您发送本数据集。联系邮箱：[email protected]
对此造成的不便，我们深感抱歉。我们将尽快推进向所有人公开数据的进程。
We release three versions of KuaiRand for different usage:
- KuaiRand-27K (23GB logs +23GB features): the complete KuaiRand dataset that has over 27K users and 32 millions videos.
- KuaiRand-1K (829MB logs + 3.5GB features): randomly sample 1,000 users from KuaiRand-27K, then remove all irrelevant videos. There are 4 millions videos rest.
- KuaiRand-Pure (184MB logs + 10MB features): only keeps the logs for the 7583 videos in the candidate pool.
The user_id and video_id are re-indexed. A visualization of their ID spaces is shown as follows.
| Dataset | Collection Policy | #Users | #Items | #Interactions | #User features | #Item features | Feedback | Timestamp |
|---|---|---|---|---|---|---|---|---|
| KuaiRand-27K | 15 policies | 27,285 | 32,038,725 | 322,278,385 | 30 | 62 | 12 signals | ✔️ |
| Random policy | 27,285 | 7,583 | 1,186,059 | 30 | 62 | 12 signals | ✔️ | |
| KuaiRand-1K | 15 policies | 1,000 | 4,369,953 | 11,713,045 | 30 | 62 | 12 signals | ✔️ |
| Random policy | 1,000 | 7,388 | 43,028 | 30 | 62 | 12 signals | ✔️ | |
| KuaiRand-Pure | 15 policies | 27,285 | 7,551 | 1,436,609 | 30 | 62 | 12 signals | ✔️ |
| Random policy | 27,285 | 7,583 | 1,186,059 | 30 | 62 | 12 signals | ✔️ |
-
- Your research needs rigorous sequential logs, such as off-policy evaluation (OPE), Reinforcement learning (RL), or long sequential recommendation.
-
- The sequential information is not necessary in your research OR If you are OK with the incomplete sequential logs. For example, if you are studying debiasing in collaborative filtering models or multi-task modeling in recommendation.
- If your model can only run with small-size data.
The file structure of the three datasets are listed as follows:
KuaiRand-27K
├── data (46GB)
│ ├── log_random_4_22_to_5_08_27k.csv (83MB)
│ ├── log_standard_4_08_to_4_21_27k_part1.csv (4.8GB)
│ ├── log_standard_4_08_to_4_21_27k_part2.csv (4.8GB)
│ ├── log_standard_4_22_to_5_08_27k_part1.csv (6.6GB)
│ ├── log_standard_4_22_to_5_08_27k_part2.csv (6.6GB)
│ ├── user_features_27k.csv (3.4MB)
│ ├── video_features_basic_27k.csv (2.6GB)
│ ├── video_features_statistic_27k_part1.csv (6.7GB)
│ ├── video_features_statistic_27k_part2.csv (6.7GB)
│ └── video_features_statistic_27k_part3.csv (6.7GB)
└── load_data_27k.pyKuaiRand-1K
├── data (4.3GB)
│ ├── log_random_4_22_to_5_08_1k.csv (2.9MB)
│ ├── log_standard_4_08_to_4_21_1k.csv (368MB)
│ ├── log_standard_4_22_to_5_08_1k.csv (481MB)
│ ├── user_features_1k.csv (132KB)
│ ├── video_features_basic_1k.csv (368MB)
│ └── video_features_statistic_1k.csv (3.1GB)
└── load_data_1k.pyKuaiRand-Pure
├── data (194MB)
│ ├── log_random_4_22_to_5_08_pure.csv (83MB)
│ ├── log_standard_4_08_to_4_21_pure.csv (80MB)
│ ├── log_standard_4_22_to_5_08_pure.csv (21MB)
│ ├── user_features_pure.csv (3.4MB)
│ ├── video_features_basic_pure.csv (612KB)
│ └── video_features_statistic_pure.csv (6.3MB)
└── load_data_pure.pyThere are three log files:
log_random_4_22_to_5_08.csvcontains all interactions resulted from random intervention.log_standard_4_22_to_5_08.csvcontains all interactions of standard recommendation.log_standard_4_08_to_4_21.csvcontains all interactions of standard recommendation for the same users in the previous two weeks (2022.04.08 ~ 2022.04.21).
| Field Name: | Description | Type | Example |
|---|---|---|---|
| user_id | The ID of the video. | int64 | 17387 |
| video_id | The ID of the video. | int64 | 1123453 |
| date | The date of this interaction | int64 | 20220421 |
| hourmin | The time of this interaction (format: HHSS). | int64 | 400 |
| time_ms | The timestamp of this interaction in millisecond. | int64 | 1650485801301 |
| is_click | A binary feedback signal. In the two-column UI, it indicates a click; In the single-column UI, it means valid_play: which equals 1 when: play_time_ms >= duration_ms if duration_ms <= 7,000 ms, or play_time_ms > 7,000 ms if duration_ms > 7,000 ms. |
int64 | 1 |
| is_like | A binary feedback signal indicating the user hit the like button. |
int64 | 0 |
| is_follow | A binary feedback signal indicating the user hit the follow the author button. |
int64 | 0 |
| is_comment | A binary feedback signal indicating the user writed comment in the comments section of this video | int64 | 0 |
| is_forward | A binary feedback signal indicating the user forwarded this video. | int64 | 0 |
| is_hate | A binary feedback signal indicating the user hated this video. | int64 | 0 |
| long_view | A binary feedback signal. It equals 1 when: play_time_ms >= duration_ms if duration_ms <= 18,000 ms, or play_time_ms >=18,000 ms if duration_ms > 18,000 ms. |
int64 | 1 |
| play_time_ms | The user's view time in millisecond. | int64 | 151024 |
| duration_ms | The video's duration time in millisecond. | int64 | 104400 |
| profile_stay_time | The time that the user stayed in this author's profile. | int64 | 0 |
| comment_stay_time | The time that the user stayed in the comments section of this video | int64 | 0 |
| is_profile_enter | A binary feedback signal indicating the user enters the author profile | int64 | 0 |
| is_rand | A binary signal indicating if this video is generated by the random intervention (i.e., a random exposed video). | int64 | 0 |
| tab | indicating the scenario of this interaction, e.g., in the recommendation page or the main page of the App. In range of [0,14]. | int64 | 1 |
| Field Name: | Description | Type | Example |
|---|---|---|---|
| user_id | The ID of the user. | int64 | 25621 |
| user_active_degree | In the set of {'high_active', 'full_active', 'middle_active', 'UNKNOWN'}. | str | "full_active" |
| is_lowactive_period | Is this user in its low active period | int64 | 0 |
| is_live_streamer | Is this user a live streamer？ | int64 | 0 |
| is_video_author | Has this user uploaded any video？ | int64 | 1 |
| follow_user_num | The number of users that this user follows. | int64 | 5 |
| follow_user_num_range | The range of the number of users that this user follows. In the set of {'0', '(0,10]', '(10,50]', '(100,150]', '(150,250]', '(250,500]', '(50,100]', '500+'} | str | "(0,10]" |
| fans_user_num | The number of the fans of this user. | int64 | 312 |
| fans_user_num_range | The range of the number of fans of this user. In the set of {'0', '[1,10)', '[10,100)', '[100,1k)', '[1k,5k)', '[5k,1w)', '[1w,10w)'} | str | "[100,1k)" |
| friend_user_num | The number of friends that this user has. | int64 | 0 |
| friend_user_num_range | The range of the number of friends that this user has. In the set of {'0', '[1,5)', '[5,30)', '[30,60)', '[60,120)', '[120,250)', '250+'} | str | "0" |
| register_days | The days since this user has registered. | int64 | 3624 |
| register_days_range | The range of the registered days. In the set of {'15-30', '31-60', '61-90', '91-180', '181-365', '366-730', '730+'}. | str | "730+" |
| onehot_feat0 | An encrypted feature of the user. Each value indicate the position of "1" in the one-hot vector. Range: {0,1} | int64 | 1 |
| onehot_feat1 | An encrypted feature. Range: {0, 1, ..., 6} | int64 | 2 |
| onehot_feat2 | An encrypted feature. Range: {0, 1, ..., 49} | int64 | 2 |
| onehot_feat3 | An encrypted feature. Range: {0, 1, ..., 1470} | int64 | 1153 |
| onehot_feat4 | An encrypted feature. Range: {0, 1, ..., 14} | int64 | 4 |
| onehot_feat5 | An encrypted feature. Range: {0, 1, ..., 33} | int64 | 0 |
| onehot_feat6 | An encrypted feature. Range: {0, 1, 2} | int64 | 0 |
| onehot_feat7 | An encrypted feature. Range: {0, 1, ..., 117} | int64 | 31 |
| onehot_feat8 | An encrypted feature. Range: {0, 1, ..., 453} | int64 | 354 |
| onehot_feat9 | An encrypted feature. Range: {0, 1, ..., 6} | int64 | 3 |
| onehot_feat10 | An encrypted feature. Range: {0, 1, ..., 4} | int64 | 3 |
| onehot_feat11 | An encrypted feature. Range: {0, 1, ..., 4} | int64 | 2 |
| onehot_feat12 | An encrypted feature. Range: {0, 1} | int64 | 1 |
| onehot_feat13 | An encrypted feature. Range: {0, 1} | int64 | 0 |
| onehot_feat14 | An encrypted feature. Range: {0, 1} | int64 | 0 |
| onehot_feat15 | An encrypted feature. Range: {0, 1} | int64 | 0 |
| onehot_feat16 | An encrypted feature. Range: {0, 1} | int64 | 0 |
| onehot_feat17 | An encrypted feature. Range: {0, 1} | int64 | 0 |
| Field Name: | Description | Type | Example |
|---|---|---|---|
| video_id | The ID of the video. | int64 | 3784 |
| author_id | The ID of the author of this video. In the range of [0, 8839734] | int64 | 441 |
| video_type | Type of this video (NORMAL or AD). | str | "NORMAL" |
| upload_dt | Upload date of this video. | str | "2020-07-08" |
| upload_type | The upload type of this video. | str | "ShortImport" |
| visible_status | The visible state of this video on the APP now. | int | 1 |
| video_duration | The time duration of this duration (in millisecond). | Int64 | 17200.0 |
| server_width | The width of this video on the server. | int64 | 720 |
| server_height | The height of this video on the server. | int64 | 1280 |
| music_id | Background music ID of this video. | int64 | 989206467 |
| music_type | Background music type of this video. | int64 | 4 |
| tag | A list of key categories (labels) of this video. | str | "12,65" |
show_cnt=80 on the main page and show_cnt=65 on the recommendation page of the App)
| Field Name: | Description | Type | Example |
|---|---|---|---|
| video_id | The ID of the video. | int64 | 9288071 |
| counts | The number of statistics. | int64 | 66 |
| show_cnt | The number of shows of this video (averaged on each day and each scenario over the one month. This applies to all the following fields) | float64 | 75.212 |
| show_user_num | The number of users who received the recommendation of this video. | float64 | 66.985 |
| play_cnt | The number of plays. | float64 | 9.409 |
| play_user_num | The number of users who plays this video. | float64 | 8.121 |
| play_duration | The total time duration of playing this video (in millisecond). | float64 | 93700.333 |
| complete_play_cnt | The number of complete plays. complete play: finishing playing the whole video, i.e., #(play_duration >= video_duration). |
float64 | 0.182 |
| complete_play_user_num | The number of users who perform the complete play. | float64 | 0.182 |
| valid_play_cnt | valid play: play_duration >= video_duration if video_duration <= 7s, or play_duration > 7 if video_duration > 7s. |
float64 | 3.545 |
| valid_play_user_num | The number of users who perform the complete play. | float64 | 3.136 |
| long_time_play_cnt | long time play: play_duration >= video_duration if video_duration <= 18s, or play_duration >=18 if video_duration > 18s. |
float64 | 1.909 |
| long_time_play_user_num | The number of users who perform the long time play. | float64 | 1.848 |
| short_time_play_cnt | short time play: play_duration < min(3s, video_duration). |
float64 | 5.015 |
| short_time_play_user_num | The number of users who perform the short time play. | float64 | 4.545 |
| play_progress | The average video playing ratio (=play_duration/video_duration) |
float64 | 0.016 |
| comment_stay_duration | Total time of staying in the comments section | float64 | 2302.712 |
| like_cnt | Total likes | float64 | 0.303 |
| like_user_num | The number of users who hit the "like" button. | float64 | 0.303 |
| click_like_cnt | The number of the "like" resulted from double click | float64 | 0.030 |
| double_click_cnt | The number of users who double click the video. | float64 | 0.273 |
| cancel_like_cnt | The number of likes that are cancelled by users. | float64 | 0.485 |
| cancel_like_user_num | The number of users who cancel their like. | float64 | 0.485 |
| comment_cnt | The number of comments within this day. | float64 | 0.015 |
| comment_user_num | The number of users who comment this video. | float64 | 0.015 |
| direct_comment_cnt | The number of direct comments (depth=1). | float64 | 0.015 |
| reply_comment_cnt | The number of reply comments (depth>1). | float64 | 0.000 |
| delete_comment_cnt | The number of deleted comments. | float64 | 0.015 |
| delete_comment_user_num | The number of users who delete their comments. | float64 | 0.015 |
| comment_like_cnt | The number of comment likes. | float64 | 0.000 |
| comment_like_user_num | The number of users who like the comments. | float64 | 0.000 |
| follow_cnt | The number of increased follows from this video. | float64 | 0.000 |
| follow_user_num | The number of users who follow the author of this video due to this video. | float64 | 0.000 |
| cancel_follow_cnt | The number of decreased follows from this video. | float64 | 0.000 |
| cancel_follow_user_num | The number of users who cancel their following of the author of this video due to this video. | float64 | 0.000 |
| share_cnt | The times of successfully sharing this video. | float64 | 0.000 |
| share_user_num | The number of users who succeed to share this video. | float64 | 0.000 |
| download_cnt | The times of downloading this video. | float64 | 0.030 |
| download_user_num | The number of users who download this video. | float64 | 0.030 |
| report_cnt | The times of reporting this video. | float64 | 0.000 |
| report_user_num | The number of users who report this video. | float64 | 0.000 |
| reduce_similar_cnt | The times of reducing similar content of this video. | float64 | 0.015 |
| reduce_similar_user_num | The number of users who choose to reduce similar content of this video. | float64 | 0.015 |
| collect_cnt | The times of adding this video to favorite videos. | float64 | 0.061 |
| collect_user_num | The number of users who add this video to their favorite videos. | float64 | 0.061 |
| cancel_collect_cnt | The times of removing this video from favorite videos. | float64 | 0.091 |
| cancel_collect_user_num | The number of users who remove this video from their favorite videos | float64 | 0.091 |
| direct_comment_user_num | The number of users who write comments directly under this video (level=1). | float64 | 0.015 |
| reply_comment_user_num | The number of users who reply the existing comments (level>1). | float64 | 0.000 |
| share_all_cnt | The times of sharing this video (no need to be successful). | float64 | 0.015 |
| share_all_user_num | The number of users who share this video (no need to be successful). | float64 | 0.015 |
| outsite_share_all_cnt | The times of sharing this video outside Kuaishou App. | float64 | 0.000 |