一、ZhihuRec介绍

ZhihuRec数据集由 清华大学信息检索组（THUIR）和 知乎公司 共同构建，仅供研究使用。ZhihuRec 数据集是从知识共享平台（知乎）收集的，该平台由 10 天内收集的约 一亿(100M) 次交互、798K 用户、165K 问题、554K 答案、240K 作者、70K 主题和超过 501K 用户查询日志组成。 还有用户、答案、问题、作者和主题的描述，这些都是匿名的。 据我们所知，这是用于个性化推荐的最大的真实世界交互数据集。由于ZhihuRec数据集包含约100M的用户回答印象日志，因此也称为ZhihuRec-100M。 还构建了从 ZhihuRec-100M 数据集随机采样的两个较小的数据集，分别称为 ZhihuRec-20M 和 ZhihuRec-1M，以满足各种应用需求。 它们包含大约 20M 和 1M 的用户回答印象日志，可以看作是一个中等大小的数据集和一个相对较小的数据集。


ZhihuRec项目及下载地址



二、数据集详情

2.1 数据集内的文件

Filename Size Description
inter_impression.csv 2.6GB user clicks and impressions
inter_query.csv 111MB user queries
info_user.csv 135MB the features of the users occured in the dataset
info_answer.csv 917MB the features of the answers occured in the dataset
info_question.csv 14MB the features of the questions occured in the dataset
info_author.csv 3.1MB the features of the authors occured in the dataset
info_topic.csv 413KB the IDs of the topics occured in the dataset
info_token.csv 409MB the features of the tokens occured in the dataset

2.2 数据集统计信息

Dataset ZhihuRec-100M ZhihuRec-20M ZhihuRec-1M
#impressions 99,978,523 19,999,857 999,970
#clicks 26,981,583 5,402,345 268,656
#clicks : #non-clicks 1 : 2.71 1 : 2.70 1 : 2.72
#queries 3,899,553 776,201 38,422
#users 798,086 159,642 7,974
avg #impressions per user 125.27 125.28 125.40
avg #clicks per user 33.81 33.84 33.69
#users with queries 501,893 100,271 5,047
avg #queries per user 7.77 7.74 7.61
#answers 554,976 343,103 81,563
#questions 165,012 104,130 29,340
#authors 240,956 167,796 47,888
#topics 72,318 54,785 22,897
#tokens 556,546 428,334 249,586

2.3 数据集字段

Some fields in the data set are null, which are represented by empty strings in the file.

inter_impression.csv

Index Nullable Description
0 user ID
1 answer ID
2 impression timestamp
3 click timestamp (0 for non-click)

inter_query.csv

Index Nullable Description
0 user ID
1 token IDs in the query (separated by spaces)
2 query timestamp

info_user.csv

Index Nullable Description
0 user ID
1 register timestamp
2 gender
3 login frequency
4 #followers
5 #topics followed by this user
6 #questions followed by this user
7 #answers
8 #questions
9 #comments
10 #thanks received by this user
11 #comments received by this user
12 #likes received by this user
13 #dislikes received by this user
14 register type
15 register platform
16 from android or not
17 from iphone or not
18 from ipad or not
19 from pc or not
20 from mobile web or not
21 device model
22 device brand
23 platform
24 province
25 city
26 $\sqrt{}$ topic IDs followed by this user (separated by spaces)

info_answer.csv

Index Nullable Description
0 answer ID
1 $\sqrt{}$ question ID
2 anonymous or not
3 $\sqrt{}$ author ID (null for anonymous)
4 labeled high-value answer or not
5 recommended by the editor or not
6 create timestamp
7 contain pictures or not
8 contain videos or not
9 #thanks
10 #likes
11 #comments
12 #collections
13 #dislikes
14 #reports
15 #helpless
16 $\sqrt{}$ token IDs in the answer (separated by spaces)
17 $\sqrt{}$ topic IDs of the answer (separated by spaces)

info_question.csv

Index Nullable Description
0 question ID
1 create timestamp
2 #answers
3 #followers
4 #invitations
5 #comments
6 $\sqrt{}$ token IDs in the question (separated by spaces)
7 $\sqrt{}$ topic IDs of the queation (separated by spaces)

info_author.csv

Index Nullable Description
0 author ID
1 is excellent author or not
2 #followers
3 is excellent answerer or not

info_topic.csv

Index Nullable Description
0 topic ID

info_token.csv

Index Nullable Description
0 token ID
1 word vector trained by word2vec (64 dimensions, separated by spaces)

ZhihuRec can’t provide the corresponding text of tokens for privacy reasons. Researchers can use word vectors in the dataset or train word vectors from scratch.



引用说明

ZhihuRec dataset can be downloaded from here, and it is for the paper:

Bin Hao, Min Zhang, Weizhi Ma, Shaoyun Shi, Xinxing Yu, Houzhi Shan, Yiqun Liu and Shaoping Ma, 2021, A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing. arXiv preprint arXiv:2106.06467.

please cite the paper if you use this dataset:

@misc{hao2021largescale,
      title={A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing},
      author={Bin Hao and Min Zhang and Weizhi Ma and Shaoyun Shi and Xinxing Yu and Houzhi Shan and Yiqun Liu and Shaoping Ma},
      year={2021},
      eprint={2106.06467},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}



广而告之