README.md

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, 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. 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. In SIGIR 2023.
  • Weiyu Cheng, Yanyan Shen, Linpeng Huang. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. 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
  ```