ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations

Python-based source-code for reproducing our work published in ACM Transactions of Intelligent Systems and Technology [1]. We use five public datasets: MovieLens 1M [2], Douban [3], LastFM [4], Ciao [5], and Goodreads [6, 7].

Usage

For reproducing our experiments with DP on the MovieLens 1M dataset, please run

python rating_prediction.py --dataset_name ml-1m --use_dp True

For combining neighborhood reuse with NeuCF [8], run

python NeuReuse.py --dataset_name ml-1m --generate_embeddings True --generate_recommendations True 

The analysis of the recommendations (evaluation of recommendation accuracy and user privacy) can be found in Rating Prediction Visualizations.ipynb and results/NeuReuse/Visualization.ipynb respectively.

Requirements

• python 3
• numpy
• pandas
• sklearn
• tensorflow
• matplotlib
• cython
• suprise
• pickle

(for detail see requirements.txt)

Contributors

• Peter Müllner, Know-Center GmbH, pmuellner [AT] know [minus] center [DOT] at (Contact)
• Elisabeth Lex, Graz University of Technology
• Markus Schedl, Johannes Kepler University Linz and Linz Institute of Technology
• Dominik Kowald, Know-Center GmbH and Graz University of Technology

References

[1] Peter Müllner, Elisabeth Lex, Markus Schedl, and Dominik Kowald. 2023. ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations. ACM Trans. Intell. Syst. Technol. 14, 5, Article 80 (October 2023), 29 Pages. https://doi.org/10.1145/3608481

[2] F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5, 4 (2015), 1–19.

[3] Longke Hu, Aixin Sun, and Yong Liu. 2014. Your neighbors affect your ratings: On geographical neighborhood influence to rating prediction. In Proc. of SIGIR’14

[4] Dominik Kowald, Markus Schedl, and Elisabeth Lex. 2020. The unfairness of popularity bias in music recommendation: A reproducibility study. In Proc. of ECIR'20

[5] Guibing Guo, Jie Zhang, Daniel Thalmann, and Neil Yorke-Smith. 2014. ETAF: An extended trust antecedents framework for trust prediction. In Proc. of ASONAM’14.

[6] Mengting Wan and Julian J. McAuley. 2018. Item recommendation on monotonic behavior chains. In Proc. of ACM RecSys’18. 86–94.

[7] Mengting Wan, Rishabh Misra, Ndapa Nakashole, and Julian J. McAuley. 2019. Fine-grained spoiler detection from large-scale review corpora. In Proc. of ACL’19. Association for Computational Linguistics, 2605–2610.

[8] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proc. of WWW’17. 173–182.