Published December 12, 2022 | Version 1.0
Dataset Open

Datasets to Evaluate Accuracy, Miscalibration and Popularity Lift in Recommendations

  • 1. Know-Center GmbH & TU Graz


This repository contains three datasets for evaluating accuracy, miscalibration and popularity lift in recommender systems. All datasets contain genre/category information in addition to different user group splits:

  1. (, based on the LFM-1b dataset of JKU Linz (
  2. MovieLens (, based on MovieLens-1M dataset (
  3. MyAnimeList (, based on the MyAnimeList dataset of Kaggle (

'user_events_cats.txt' contains the users' rating/interaction data along with a list of genres/categories assigend to the rated items. The list of categories is given in 'categories.txt'. Additionally, assignments to three user groups that differ in their inclination to popular/mainstream items are provided: LowPop in 'low_main_users.txt', MedPop in 'med_main_users.txt', and HighPop in '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,cats", where different categories are separated by '|'

The format of the categories files are "category-name,index", where index refers to the category-id in the user-events files

Example Python-code for analyzing the datasets as well as empirical results on calibration, popularity lift and accuracy can be found on GitHub:


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