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<p>Personalized recommendation algorithms, designed to cater to users’ individualized preferences and enhance the efficiency of Internet information services, have inadvertently precipitated significant legal risks, notably the potential for user privacy breaches. This development necessitates prompt and appropriate legal responses. This paper presents an approach utilizing cosine similarity and Pearson correlation to establish a set of nearest neighbors, facilitating the identification and querying of the closest users based on similarity metrics. Subsequently, we implement a collaborative filtering recommendation algorithm tailored to these personalized settings. We thoroughly evaluate the legal risks associated with the application of customized recommendation algorithms, supplemented by detailed case analyses. The Pareto chart of risk crisis factors reveals that the frequency of issues related to policy legislation and technical concerns are 165 and 95, respectively, cumulatively accounting for 46.68% of the probability share—nearly half of all identified factors. This highlights the critical need for focused attention in these areas. Furthermore, our analysis calculates the probability and severity of data leakage across various paths within the recommendation process. Results indicate severities of 0.029, 0.0333, 0.0409, 0.0447, 0.0481, and 0.165, with the most significant vulnerability appearing in the final recommendation phase to the user, underscoring the imperative to prioritize data privacy in this module. In response, we propose modifications to the existing models to incorporate principles of differential privacy, aiming to mitigate these legal risks and enhance the security and reliability of personalized recommendation algorithms. This initiative is essential for aligning technological advancements with stringent privacy standards and legal requirements.</p>
</abstract>

Personalized recommendation algorithms, designed to cater to users’ individualized preferences and enhance the efficiency of Internet information services, have inadvertently precipitated significant legal risks, notably the potential for user privacy breaches. This development necessitates prompt and appropriate legal responses. This paper presents an approach utilizing cosine similarity and Pearson correlation to establish a set of nearest neighbors, facilitating the identification and querying of the closest users based on similarity metrics. Subsequently, we implement a collaborative filtering recommendation algorithm tailored to these personalized settings. We thoroughly evaluate the legal risks associated with the application of customized recommendation algorithms, supplemented by detailed case analyses. The Pareto chart of risk crisis factors reveals that the frequency of issues related to policy legislation and technical concerns are 165 and 95, respectively, cumulatively accounting for 46.68% of the probability share—nearly half of all identified factors. This highlights the critical need for focused attention in these areas. Furthermore, our analysis calculates the probability and severity of data leakage across various paths within the recommendation process. Results indicate severities of 0.029, 0.0333, 0.0409, 0.0447, 0.0481, and 0.165, with the most significant vulnerability appearing in the final recommendation phase to the user, underscoring the imperative to prioritize data privacy in this module. In response, we propose modifications to the existing models to incorporate principles of differential privacy, aiming to mitigate these legal risks and enhance the security and reliability of personalized recommendation algorithms. This initiative is essential for aligning technological advancements with stringent privacy standards and legal requirements.

Legal Risk Prevention for Personalized Recommendation Algorithms

The National Police University for Criminal Justice, Baoding

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Legal Risk Prevention for Personalized Recommendation Algorithms"}

Personalized recommendation algorithms, designed to cater to users’ individualized preferences and enhance the efficiency of Internet information services,...

Legal Risk Prevention for Personalized Recommendation Algorithms

Published Online: Aug 05, 2024

Page range: -

Received: Apr 08, 2024

Accepted: Jun 22, 2024

DOI: https://doi.org/10.2478/amns-2024-1859

Keywords
Personalized recommendation, Cosine similarity, Pearson correlation, Collaborative filtering, Differential privacy, Legal risk prevention

© 2024 Wenyu Hu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Legal Risk Prevention for Personalized Recommendation Algorithms

Published Online: Aug 05, 2024

Page range: -

Received: Apr 08, 2024

Accepted: Jun 22, 2024

DOI: https://doi.org/10.2478/amns-2024-1859

KeywordsPersonalized recommendation, Cosine similarity, Pearson correlation, Collaborative filtering, Differential privacy, Legal risk prevention

© 2024 Wenyu Hu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Personalized recommendation, Cosine similarity, Pearson correlation, Collaborative filtering, Differential privacy, Legal risk prevention