AiresPucrs/movielens-user-ratings

---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: userId
dtype: int64
- name: movieId
dtype: int64
- name: rating
dtype: float64
- name: timestamp
dtype: int64
splits:
- name: train
num_bytes: 3226752
num_examples: 100836
download_size: 1166644
dataset_size: 3226752
license: other
language:
- en
pretty_name: 'MovieLens User Ratings '
size_categories:
- 100K<n<1M
task_categories:
- text-classification
tags:
- movies
---
# MovieLens User Ratings (Teeny-Tiny Castle)

This dataset is part of a tutorial tied to the [Teeny-Tiny Castle](https://github.com/Nkluge-correa/TeenyTinyCastle), an open-source repository containing educational tools for AI Ethics and Safety research.

## How to Use

```python
from datasets import load_dataset

dataset = load_dataset("AiresPucrs/movielens-user-ratings", split = 'train')
```

This dataset contains a set of movie ratings from the MovieLens website, a movie recommendation service. The dataset was collected by the GroupLens Research Project at the University of Minnesota. The MovieLens 100K movie ratings dataset contains 100,000 ratings (1-5 stars) from 943 users on 1682 movies. The dataset includes four main features: userId, movieId, rating, and timestamp.

原始信息汇总

数据集概述

数据集名称

movielens-user-ratings

数据集来源

该数据集包含来自MovieLens网站的电影评分数据，MovieLens是一个电影推荐服务。数据由明尼苏达大学的GroupLens研究项目收集并提供。

数据集详情

• 语言: 英语
• 总大小: 100,836条记录
• 数据文件: 包含多个CSV文件，本数据集仅使用"ratings.csv"文件。

数据集结构

特征

• userId: 用户唯一标识符，数据类型为int64。
• movieId: 电影唯一标识符，数据类型为int64。
• rating: 评分，五星级评分制，数据类型为float64。
• timestamp: 评分时间戳，数据类型为int64。

数据分割

• train: 训练集，包含3226752字节，100836条记录。

数据集大小

• 下载大小: 1166644字节
• 数据集大小: 3226752字节

许可证

该数据集的许可证为"other"。

引用

latex @article{10.1145/2827872, author = {Harper, F. Maxwell and Konstan, Joseph A.}, title = {The MovieLens Datasets: History and Context}, year = {2015}, issue_date = {January 2016}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {5}, number = {4}, issn = {2160-6455}, url = {https://doi.org/10.1145/2827872}, doi = {10.1145/2827872}, journal = {ACM Trans. Interact. Intell. Syst.}, month = dec, articleno = {19}, numpages = {19}, keywords = {Datasets, recommendations, ratings, MovieLens} }

使用方法

python from datasets import load_dataset

dataset = load_dataset("AiresPucrs/movielens-user-ratings", split=train)