krishnakamath/movielens-32m-sequential-recommender

MovieLens 32M Sequential Recommender Dataset

This dataset is a processed version of the MovieLens 32M dataset, specifically formatted for sequential recommendation tasks. It contains user-item interaction sequences, enriched with rating and timestamp information, split into training, validation, and test sets.

Dataset Structure

The dataset is provided as a DatasetDict with three splits: train, validation, and test. Each split contains:

• —input_sequence: A string representing a user's interaction history. Each interaction is formatted as movieId:rating:timestamp. Sequences vary in length and starting points to provide diverse training examples.
• —target_item: A string representing the movieId of the next item the user interacted with, which the model is expected to predict.

Generation Parameters

This dataset was generated with the following parameters:

• —NUM_USERS: 50000 (Number of unique users included in the dataset)
• —MAX_SEQUENCES_PER_USER: 5 (Maximum number of training sequences sampled from each user's history)

These parameters are also embedded in the dataset's metadata for reproducibility.

Citation

Please cite the original MovieLens dataset if you use this data in your research:

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872

Acknowledgement

The Python scripts used to generate and process this dataset were developed with the assistance of Google's Gemini.