This repository includes the source code of the paper, which is titled "Exploring potential biases towards blockbuster items in ranking-based recommendations" published in "Data Mining and Knowledge Discover" journal of Springer. 

Initial Step
Get predictions for ten algorithms on five datasets via Surprise library in Python (see Predict.py) and save predicted scores as "DatasetName-AlgorithmName.txt" and then transform them as .mat format as in their corresponding folder in the Predictions file, which is shared below. In the Datasets folder, we store the original datasets as .mat files. The ML-100K and ML-1M datasets are stored as user-item matrix form, while other datasets are stored as userID|itemID|Rating as nx3 format where n is the total number of provided ratings.

The predictions file is shared via the google drive link below:
https://drive.google.com/file/d/1UFnA48yks22rnGKVNMHMSROLA-FhV0JY/view?usp=sharing