KuaiRec is a real-world dataset collected from the recommendation logs of the video-sharing mobile app Kuaishou. For now, it is the first dataset that contains a fully observed user-item interaction matrix. For the term “fully observed”, we mean there are almost no missing values in the user-item matrix, i.e., each user has viewed each video and then left feedback.
Other related open-sourced datasets are KuaiRand and KuaiSAR.
The following figure illustrates the user-item matrices in traditional datasets and KuaiRec.
With all user preferences known, KuaiRec can used in offline evaluation (i.e., offline A/B test) for recommendation models. It can benefit lots of research directions, such as unbiased recommendation, interactive/conversational recommendation, reinforcement learning (RL), and off-policy evaluation (OPE) for recommendation.
If you use it in your work, please cite our paper:
@inproceedings{gao2022kuairec,
author = {Gao, Chongming and Li, Shijun and Lei, Wenqiang and Chen, Jiawei and Li, Biao and Jiang, Peng and He, Xiangnan and Mao, Jiaxin and Chua, Tat-Seng},
title = {KuaiRec: A Fully-Observed Dataset and Insights for Evaluating Recommender Systems},
booktitle = {Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
series = {CIKM '22},
location = {Atlanta, GA, USA},
url = {https://doi.org/10.1145/3511808.3557220},
doi = {10.1145/3511808.3557220},
numpages = {11},
year = {2022},
pages = {540–550}
}
This repository lists the example codes in evaluating conversational recommendations as described in the paper.
We provide some simple statistics of this dataset here. It is generated by Statistics_KuaiRec.ipynb. You can do it online at Google Colab 
.
News!
2024.06.02: To facilitate the application of large language models (LLM) in recommendation systems, we collect caption information and category information for all videos and presented them in text format!
- The corresponding caption and category information can be downloaded here: kuairec_caption_category.csv. Additionally, we have packaged them into the
KuaiRec.zipfile, which can be downloaded from the download section below. - The descriptions of the caption and category information are available here.
2022.05.16: We update the dataset to version 2.0. We made the following changes:
- We removed the unused video
ID=1225from all tables having the fieldvideo_idand re-indexed the rest videos, i.e.,ID = ID - 1 if ID > 1225. - We added two tables to enhance the side information for users and videos, respectively. See
4.item_daily_features.csvand5. user_features.csvunder the data description section for details.
Download the data
We provide several options to download this dataset:
Option 1. Download via the “wget” command.
wget https://nas.chongminggao.top:4430/datasets/KuaiRec.zip --no-check-certificate
unzip KuaiRec.zip
Option 2. Download manually through the following links:
-
Optional link 1: Google Drive
-
Optional link 2: USTC Drive (中科大)
The script loaddata.py provides a simple way to load the data via Pandas in Python.
Data Descriptions
KuaiRec contains millions of user-item interactions as well as side information including the item categories and a social network. Six files are included in the download data:
KuaiRec
├── data
│ ├── big_matrix.csv
│ ├── small_matrix.csv
│ ├── social_network.csv
│ ├── user_features.csv
│ ├── item_daily_features.csv
│ └── item_categories.csv
│ └── kuairec_caption_category.csv
The statistics of the small matrix and big matrix in KuaiRec.
| #Users | #Items | #Interactions | Density | |
|---|---|---|---|---|
| small matrix | 1,411 | 3,327 | 4,676,570 | 99.6% |
| big matrix | 7,176 | 10,728 | 12,530,806 | 16.3% |
Note that the density of the small matrix is 99.6% instead of 100% because some users have explicitly indicated that they would not be willing to receive recommendations from certain authors. I.e., They blocked these videos.
1. Descriptions of the fields in big_matrix.csv and small_matrix.csv.
| Field Name: | Description | Type | Example |
|---|---|---|---|
| user_id | The ID of the user. | int64 | 0 |
| video_id | The ID of the viewed video. | int64 | 3650 |
| play_duration | Time of video viewing of this interaction (millisecond). | int64 | 13838 |
| video_duration | Time of this video (millisecond). | int64 | 10867 |
| time | Human-readable date for this interaction | str | “2020-07-05 00:08:23.438” |
| date | Date of this interaction | int64 | 20200705 |
| timestamp | Unix timestamp | float64 | 1593878903.438 |
| watch_ratio | The video watching ratio (=play_duration/video_duration) | float64 | 1.273397 |
The “watch_ratio” can be deemed as the label of the interaction. Note: there is no “like” signal for this dataset. If you need this binary signal in your scenarios, you can create it yourself. E.g., like = 1 if watch_ratio > 2.0.
2. Descriptions of the fields in social_network.csv
| Field Name: | Description | Type | Example |
|---|---|---|---|
| user_id | The ID of the user. | int64 | 5352 |
| friend_list | The list of IDs of the friends of this user. | list | [4202,7126] |
3. Descriptions of the fields in item_categories.csv.
| Field Name: | Description | Type | Example |
|---|---|---|---|
| video_id | The ID of the video. | int64 | 1 |
| feat | The list of tags of this video. | list | [27,9] |
4. Descriptions of the fields in item_daily_features.csv. (Added on 2022.05.16)
| Field Name: | Description | Type | Example |
|---|---|---|---|
| video_id | The ID of the video. | int64 | 3784 |
| date | Date of the statistics of this video. | int64 | 20200730 |
| author_id | The ID of the author of this video. | 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. | str | “public” |
| video_duration | The time duration of this duration (in milliseconds). | float64 | 17200.0 |
| video_width | The width of this video on the server. | int64 | 720 |
| video_height | The height of this video on the server. | int64 | 1280 |
| music_id | Background music ID of this video. | int64 | 989206467 |
| video_tag_id | The ID of the tag of this video. | int64 | 2522 |
| video_tag_name | The name of the tag of this video. | string | “祝福” |
| show_cnt | The number of shows of this video within this day (the same with all following fields) | int64 | 7716 |
| show_user_num | The number of users who received the recommendation of this video. | int64 | 5256 |
| play_cnt | The number of plays. | int64 | 7701 |
| play_user_num | The number of users who play this video. | int64 | 5034 |
| play_duration | The total time duration of playing this video (in milliseconds). | int64 | 138333346 |
| complete_play_cnt | The number of complete plays. complete play: finishing playing the whole video, i.e., #(play_duration >= video_duration). |
int64 | 3446 |
| complete_play_user_num | The number of users who perform the complete play. | int64 | 2033 |
| valid_play_cnt | valid play: play_duration >= video_duration if video_duration <= 7s, or play_duration > 7 if video_duration > 7s. |
int64 | 5099 |
| valid_play_user_num | The number of users who perform the complete play. | int64 | 3195 |
| long_time_play_cnt | long time play: play_duration >= video_duration if video_duration <= 18s, or play_duration >=18 if video_duration > 18s. |
int64 | 3299 |
| long_time_play_user_num | The number of users who perform the long time play. | int64 | 1940 |
| short_time_play_cnt | short time play: play_duration < min(3s, video_duration). |
int64 | 1538 |
| short_time_play_user_num | The number of users who perform the short time play. | int64 | 1190 |
| play_progress | The average video playing ratio (=play_duration/video_duration) |
int64 | 0.579695 |
| comment_stay_duration | Total time of staying in the comments section | int64 | 467865 |
| like_cnt | Total likes | int64 | 659 |
| like_user_num | The number of users who hit the “like” button. | int64 | 657 |
| click_like_cnt | The number of the “like” resulted from double click | int64 | 496 |
| double_click_cnt | The number of users who double-click the video. | int64 | 163 |
| cancel_like_cnt | The number of likes that are canceled by users. | int64 | 15 |
| cancel_like_user_num | The number of users who cancel their likes. | int64 | 15 |
| comment_cnt | The number of comments within this day. | int64 | 13 |
| comment_user_num | The number of users who comment on this video. | int64 | 12 |
| direct_comment_cnt | The number of direct comments (depth=1). | int64 | 13 |
| reply_comment_cnt | The number of reply comments (depth>1). | int64 | 0 |
| delete_comment_cnt | The number of deleted comments. | int64 | 0 |
| delete_comment_user_num | The number of users who delete their comments. | int64 | 0 |
| comment_like_cnt | The number of comment likes. | int64 | 2 |
| comment_like_user_num | The number of users who like the comments. | int64 | 2 |
| follow_cnt | The number of increased follows from this video. | int64 | 151 |
| follow_user_num | The number of users who follow the author of this video due to this video. | int64 | 151 |
| cancel_follow_cnt | The number of decreased follows from this video. | int64 | 0 |
| cancel_follow_user_num | The number of users who cancel their following of the author of this video due to this video. | int64 | 0 |
| share_cnt | The times of sharing this video. | int64 | 1 |
| share_user_num | The number of users who share this video. | int64 | 1 |
| download_cnt | The times of downloading this video. | int64 | 2 |
| download_user_num | The number of users who download this video. | int64 | 2 |
| report_cnt | The times of reporting this video. | int64 | 0 |
| report_user_num | The number of users who report this video. | int64 | 0 |
| reduce_similar_cnt | The times of reducing similar content of this video. | int64 | 2 |
| reduce_similar_user_num | The number of users who choose to reduce similar content of this video. | int64 | 2 |
| collect_cnt | The times of adding this video to favorite videos. | int64 | 0 |
| collect_user_num | The number of users who add this video to their favorite videos. | int64 | 0 |
| cancel_collect_cnt | The times of removing this video from favorite videos. | int64 | 0 |
| cancel_collect_user_num | The number of users who remove this video from their favorite videos | int64 | 0 |
5. Descriptions of the fields in user_features.csv (Added on 2022.05.16)
| Field Name: | Description | Type | Example |
|---|---|---|---|
| user_id | The ID of the user. | int64 | 0 |
| user_active_degree | In the set of {‘high_active’, ‘full_active’, ‘middle_active’, ‘UNKNOWN’}. | str | “high_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 | 0 |
| 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 | 0 |
| 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 | “0” |
| 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 | 107 |
| 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 | “61-90” |
| onehot_feat0 | An encrypted feature of the user. Each value indicate the position of “1” in the one-hot vector. Range: {0,1} | int64 | 0 |
| onehot_feat1 | An encrypted feature. Range: {0, 1, …, 7} | int64 | 1 |
| onehot_feat2 | An encrypted feature. Range: {0, 1, …, 29} | int64 | 17 |
| onehot_feat3 | An encrypted feature. Range: {0, 1, …, 1075} | int64 | 638 |
| onehot_feat4 | An encrypted feature. Range: {0, 1, …, 11} | int64 | 2 |
| onehot_feat5 | An encrypted feature. Range: {0, 1, …, 9} | int64 | 0 |
| onehot_feat6 | An encrypted feature. Range: {0, 1, 2} | int64 | 1 |
| onehot_feat7 | An encrypted feature. Range: {0, 1, …, 46} | int64 | 6 |
| onehot_feat8 | An encrypted feature. Range: {0, 1, …, 339} | int64 | 184 |
| onehot_feat9 | An encrypted feature. Range: {0, 1, …, 6} | int64 | 6 |
| onehot_feat10 | An encrypted feature. Range: {0, 1, …, 4} | int64 | 3 |
| onehot_feat11 | An encrypted feature. Range: {0, 1, …, 2} | int64 | 0 |
| onehot_feat12 | An encrypted feature. Range: {0, 1} | int64 | 0 |
| 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 |
6. Descriptions of the caption and category fields in kuairec_caption_category.csv (Added on 2024.06.02)
| Field Name: | Description | Type | Example |
|---|---|---|---|
| video_id | The ID of the video | int64 | 2418 |
| manual_cover_text | 封面文字 (added by its author) | str | “被小可爱发现了” |
| caption | 简介标题 (added by its author) | str | “这是什么狗狗，这么可爱真的可以这么遛吗？#喜欢的双击加关注 #直播 #博美俊介 #萌宠驾到” |
| topic_tag | Tags of the topics of this video (added by its author) | str | “[博美俊介,喜欢的双击加关注,直播,萌宠驾到]” |
| first_level_category_id | First-level category ID | int64 | 17 |
| first_level_category_name | First-level category name | str | “宠物” |
| second_level_category_id | Second-level category ID | int64 | 233 |
| second_level_category_name | Second-level category name | str | “宠物日常记录” |
| third_level_category_id | Thrid-level category ID | int64 | 1169 |
| third_level_category_name | Third-level category name | str | “宠物狗” |