AiresPucrs/movielens-user-ratings
收藏资源简介：
--- 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.
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)
