A Comparative Study for Privacy-Aware Recommendation Systems

Abstract

Recommendation systems are sophisticated processes for information filtering designed to offer users tailored recommendations based on their preferences and interests. Users need help to choose between options as the amount of information on the web grows. As a result, it is critical to deliver personalized recommendations to consumers to promote user loyalty and satisfaction. Because recommender systems use sensitive user information such as ratings, comments, likes, and dislikes, this information can be leaked if no privacy measures are taken. As a result, we presented a comparison of privacy-aware recommendation systems in this paper. Two experiments are carried out. In the first experiment, we examined collaborative filtering algorithms on perturbed ratings and then compared hybrid, collaborative, and content-based algorithms on perturbed ratings in the second experiment. According to the results, the Singular Value Decomposition++ (SVDpp) algorithm presented the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values for epsilon 100, with 0.8889 and 0.6822, respectively. Furthermore, for epsilon 100, the hybrid filtering technique had the lowest RMSE and MAE rates of 0.90664 and 0.69813, respectively.

Keywords

• Recommender System
• Privacy Protection
• Differential Privacy

References

1. Altman, I. (1976). A conceptual analysis. Environment and behavior, 8(1), 7-29. https://doi.org/10.1177/001391657600800102
2. Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M. (2019). Local Differential Privacy for Deep Learning. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2019.2952146
3. Badsha, S., Yi, X., Khalil, I., & Bertino, E. (2017). Privacy preserving user-based recommender system. IEEE 37th International Conference on Distributed Computing Systems (ICDCS). https://doi.org/10.1109/ICDCS.2017.248
4. Banisar, D., & Davies, S. (1999). Privacy and human rights: An international survey of privacy laws and practice. Global Internet Liberty Campaign. (Accessed:17/04/2023) https://gilc.org/privacy/survey/intro.html
5. Cai, G., Lee, K., & Lee, I. (2018). Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos. Expert Systems with Applications, 94, 32-40. https://doi.org/10.1016/j.eswa.2017.10.049
6. Canbay, P., & Hüseyin, T. (2022). Yapıların Isıtma ve Soğutma Yükünün Yapay Zeka ile Tahmini. International Journal of Pure and Applied Sciences, 8(2), 478-489. https://doi.org/10.29132/ijpas.1166227
7. Cunha, T., Soares, C., & de Carvalho, A. C. (2018). Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering. Information Sciences, 423, 128-144. https://doi.org/10.1016/j.ins.2017.09.050
8. Differential Privacy. (Accessed:23/05/2023), https://www.apple.com/privacy/docs/Differential_Privacy_Overview.pdf

9. Dwork, C. (2006). Differential Privacy. International Colloquium on Automata, Languages and Programming, Berlin, Heidelberg. https://doi.org/10.1007/11786986
10. Fukuchi, K., Tran, Q. K., & Sakuma, J. (2017). Differentially private empirical risk minimization with input perturbation. International Conference on Discovery Science. https://doi.org/10.1007/978-3-319-67786-6_6
11. Garanayak, M., Mohanty, S. N., Jagadev, A. K., & Sahoo, S. (2019). Recommender system using item based collaborative filtering (CF) and K-means. International Journal of Knowledge-based and Intelligent Engineering Systems, 23(2), 93-101. https://doi.org/10.3233/KES-190402
12. Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018). A hybrid approach using collaborative filtering and content based filtering for recommender system. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1000/1/012101
13. Goodbooks Dataset. (Accessed:02/06/2023) https://www.kaggle.com/datasets/zygmunt/goodbooks-10k
14. Guo, Y., Wang, M., & Li, X. (2017). Application of an improved Apriori algorithm in a mobile e-commerce recommendation system. Industrial Management & Data Systems, 117(2), 287-303. https://doi.org/10.1108/IMDS-03-2016-0094
15. Hu, M., Wu, D., Wu, R., Shi, Z., Chen, M., & Zhou, Y. (2021). RAP: A Light-Weight Privacy-Preserving Framework for Recommender Systems. IEEE Transactions on Services Computing, 15(5), 2969-2981. https://doi.org/10.1109/TSC.2021.3065035
16. Javed, U., Shaukat, K., Hameed, I. A., Iqbal, F., Alam, T. M., & Luo, S. (2021). A review of content-based and context-based recommendation systems. International Journal of Emerging Technologies in Learning (iJET), 16(3), 274-306. https://doi.org/10.3991/ijet.v16i03.18851
17. Kawasaki, M., & Hasuike, T. (2017). A recommendation system by collaborative filtering including information and characteristics on users and items. 2017 ieee symposium series on computational intelligence. https://doi.org/10.1109/SSCI.2017.8280983
18. Kunaver, M., & Požrl, T. (2017). Diversity in recommender systems–A survey. Knowledge-based systems, 123, 154-162. https://doi.org/10.1016/j.knosys.2017.02.009
19. Li, N., Li, T., & Venkatasubramanian, S. (2007). t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. IEEE International Conference on Data Engineering, Istanbul, Turkey. https://doi.org/10.1109/ICDE.2007.367856
20. Liu, X., Li, Q., Ni, Z., & Hou, J. (2019). Differentially private recommender system with autoencoders. 2019 International Conference on Internet of Things and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00094
21. Machanavajjhala, A., Gehrke, J., Kifer, D., & Venkitasubramaniam, M. (2006). l-Diversity: Privacy Beyond k-Anonymity. International Conference on Data Engineering, Atlanta, USA. https://doi.org/10.1109/ICDE.2006.1
22. Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., & Diaz, F. (2018). Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. Acm international conference on information and knowledge management. https://doi.org/10.1145/3269206.3272027
23. Milano, S., Taddeo, M., & Floridi, L. (2020). Recommender systems and their ethical challenges. AI & society, 35, 957-967. https://doi.org/10.1007/s00146-020-00950-y
24. Mirshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., & Esmaeilzadeh, H. (2020). Privacy in Deep Learning: A Survey. arXiv preprint arXiv:2004.12254. https://doi.org/10.48550/arXiv.2004.12254
25. The Movies Dataset. (Accessed:13/05/2023) https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset
26. Neera, J., Chen, X., Aslam, N., Wang, K., & Shu, Z. (2021). Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2021.3126577
27. Nergiz, M. E., Atzori, M., & Clifton, C. (2007). Hiding the Presence of Individuals from Shared Databases. ACM SIGMOD International Conference on Management of Data, Beijing, China. https://doi.org/10.1145/1247480.1247554
28. Selvaraj, S., & Gangadharan, S. S. (2021). Privacy preserving hybrid recommender system based on deep learning. Turkish Journal of Electrical Engineering and Computer Sciences, 29(5), 2385-2402. https://doi.org/10.3906/elk-2010-40
29. Sütçü, M., Ecem, K., & Erdem, O. (2021). Movie Recommendation Systems Based on Collaborative Filtering: A Case Study on Netflix. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 37(3), 367-376.
30. Sweeney, L. (2002). k-Anonymity: A Model for Protecting Privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557-570. https://doi.org/10.1142/S0218488502001648
31. Wang, J., & Wang, A. (2020). An improved collaborative filtering recommendation algorithm based on differential privacy. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). https://doi.org/10.1109/ICSESS49938.2020.9237702
32. Warren, S. D., & Brandeis, L. D. (1890). The Right to Privacy. Harvard law review, 193-220. https://doi.org/10.2307/1321160
33. Xian, Z., Li, Q., Li, G., & Li, L. (2017). New collaborative filtering algorithms based on SVD++ and differential privacy. Mathematical Problems in Engineering, 2017. https://doi.org/10.1155/2017/1975719
34. Xiong, P., Zhang, L., Zhu, T., Li, G., & Zhou, W. (2020). Private collaborative filtering under untrusted recommender server. Future Generation Computer Systems, 109, 511-520. https://doi.org/10.1016/j.future.2018.05.077
35. Yin, C., Shi, L., Sun, R., & Wang, J. (2020). Improved collaborative filtering recommendation algorithm based on differential privacy protection. The Journal of Supercomputing, 76, 5161-5174. https://doi.org/10.1007/s11227-019-02751-7
36. Yu, T., Shen, Y., & Jin, H. (2019). A visual dialog augmented interactive recommender system. ACM SIGKDD international conference on knowledge discovery & data mining. https://doi.org/10.1145/3292500.3330991
37. Zhang, S., Liu, L., Chen, Z., & Zhong, H. (2019). Probabilistic matrix factorization with personalized differential privacy. Knowledge-based systems, 183, 104864. https://doi.org/10.1016/j.knosys.2019.07.035
38. Zhao, J., Geng, X., Zhou, J., Sun, Q., Xiao, Y., Zhang, Z., & Fu, Z. (2019). Attribute mapping and autoencoder neural network based matrix factorization initialization for recommendation systems. Knowledge-based systems, 166, 132-139. https://doi.org/10.1016/j.knosys.2018.12.022