Select Language

AI社区

公开数据集

重复消耗矩阵数据集(包含用户消费的次数和物品)

重复消耗矩阵数据集(包含用户消费的次数和物品)

53.5M
635 浏览
0 喜欢
0 次下载
0 条讨论
Computer Clustering

Dimitrios Kotzias, dkotzias '@' ics.uci.edu, University of California IrvineData Set Information:There are 7 dat......

数据结构 ? 53.5M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Dimitrios Kotzias, dkotzias '@' ics.uci.edu, University of California Irvine


    Data Set Information:

    There are 7 datasets from Reddit, Twitter, Gowalla and Lastfm.
    Each matrix contains how many times a user 'consumed' and item. Items can be locations, artists, or subreddits.
    Details about each dataset are presented below. (In the parenthesis is the number of Users x Items)

    tw_oc (13k x 11k): tweets with geolocation from Orange County CA area. Items are locations a user visits in this case.
    tw_ny (30k x 11k): Same as tw_oc but from the New York area.

    go_sf (2k x 7k): Check-ins from the app Gowalla, from the San Fransisco area. Full dataset here: [Web link]
    go_ny (1k x 7k): Same as go_sf, but from the New York area.

    lastfm (992 x 15k): How many times, a user listened to each artist. Covers 3 years of listening habbits, full dataset here: [Web link]a??ocelma/[Web link]

    reddit_top (113k x 21k): How many times a user posted in a subreddit. These are the 130k most active users from 2015 and 20k most subscribed subreddits. This dataset is very large and can take a lot of time to load/use.
    reddit_sample (20k x 21k): Same as reddit_top, but a sample of 20k users.


    Attribute Information:

    The attributes represent items (categories) that uses tend to select multiple times. These can be music artists, subreddits or locations on the map.


    Relevant Papers:

    Predicting Consumption Patterns with Repeated and Novel Events by Dimitrios Kotzias, Moshe Lichman and Padhraic Smyth.



    Citation Request:

    If you have no special citation requests, please leave this field blank.

    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:9 去赚积分?
    • 635浏览
    • 0下载
    • 0点赞
    • 收藏
    • 分享