# MovielensLatest_x1

The MovieLens dataset consists of users' tagging records on movies. The task is formulated as personalized tag recommendation with each tagging record (user_id, item_id, tag_id) as an data instance. The target value denotes whether the user has assigned a particular tag to the movie. We provide the reusable, processed dataset released by [the BARS benchmark](https://openbenchmark.github.io), which are randomly split into 7:2:1 as the training set, validation set, and test set, respectively. 

### Dataset Details

+ **Repository:** https://github.com/reczoo/BARS/blob/main/datasets/MovieLens/README.md#movielenslatest_x1

+ **Used by papers:** 
    - Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong. [FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction](https://arxiv.org/abs/2304.00902). In AAAI 2023.
    - Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang. [FINAL: Factorized Interaction Layer for CTR Prediction](https://dl.acm.org/doi/10.1145/3539618.3591988). In SIGIR 2023.
    - Weiyu Cheng, Yanyan Shen, Linpeng Huang. [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://ojs.aaai.org/index.php/AAAI/article/view/5768). In AAAI 2020.

+ **Check the md5sum for data integrity:**

      ```bash
      $ md5sum train.csv valid.csv test.csv
      efc8bceeaa0e895d566470fc99f3f271 train.csv
      e1930223a5026e910ed5a48687de8af1 valid.csv
      54e8c6baff2e059fe067fb9b69e692d0 test.csv
      ```