Dataset Open Access
Fair RecSys Datasets
Kowald Dominik
Four multimedia recommender systems datasets to study popularity bias and fairness:
- Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)
- MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)
- BookCrossing (book.zip), based on the BookCrossing dataset of Uni Freiburg (http://www2.informatik.uni-freiburg.de/~cziegler/BX/)
- MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)
Each dataset contains of user interactions (user_events.txt) and three user groups that differ in their inclination to popular/mainstream items: LowPop (low_main_users.txt), MedPop (med_main_users.txt), and HighPop (high_main_users.txt).
The format of the three user files are "user,mainstreaminess"
The format of the user-events files are "user,item,preference"
Example Python-code for analyzing the datasets as well as more information on the user groups can be found on Github (https://github.com/domkowald/FairRecSys) and on Arxiv (https://arxiv.org/abs/2203.00376)
Files
(16.3 MB)
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downloads
| All versions | This version | |
|---|---|---|
| Views | 724 | 724 |
| Downloads | 109 | 109 |
| Data volume | 475.6 MB | 475.6 MB |
| Unique views | 644 | 644 |
| Unique downloads | 63 | 63 |
Indexed in
- Publication date:
- February 17, 2022
- DOI:
-
- Keyword(s):
- multimedia recommender systems fairness popularity bias
- License (for files):
- Creative Commons Attribution 4.0 International