Counterfactual Explanations for Recommendation Bias

Code and data for the paper:

L. Zafeiriou, E. Pitoura, P. Tsaparas, Counterfactual Explanations for Recommendation Bias, BIAS Workshop, ECML-PKDD, 2023.

Datasets

Folder datasets contains the real and synthetic data we used. Within this folder:

Real

Zipped folder ml-100k contains the public MovieLens dataset with 100K ratings.

Synthetic

Zipped folder synthetic contains the data we generated with different parameters (in the file name). Files .info contain metadata for the synthetic dataset. Files .edges contain the generated user-item graph in the format: <user_id_x> <item_id_y>\n (per line), meaning <user_id_x> rated <item_id_y>.

Synthetic dataset filename explanation: in e.g. synth_0.7_b_1.3_p.edges, 0.7 is the bias and 1.3 is the popularity.

For more details in synthetic datasets generation, see synthetic_gen.py.

Code

Folder src contains the code for our algorithms. To setup the necessary dependencies, there are environment.yml and requirements.txt files.

References:

• F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
• Athanasios N. Nikolakopoulos and George Karypis. 2019. RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19). Association for Computing Machinery, New York, NY, USA, 150–158. https://doi.org/10.1145/3289600.3291016