User Characterization Strategies in GDPR-Compliant Environments: Beekeeper AG Case Study

Recommendation systems often use pseudonymized personal data, which means that the names of individuals in their database can only be linked to their information using a separate key stored elsewhere. Personalization vendors are subject to the General Data Protection Regulation (GDPR) as they process personal data. As a result, they must comply with all relevant GDPR provisions with their clients. This study introduces a novel approach to user characterization in GDPR-compliant environments. The aim is to model users beyond the conventional profiling methods employed in social networks. Under the GDPR guidelines, user characterization must comply with strict consent, data minimization, and transparency regulations. This leads to a reduction in the use of personal data, which in turn affects the depth of user profiles for recommender systems (RSs). Our approach involves a hybrid RS that leverages user interactions on a platform while keeping the content of the posts inaccessible. We use behavioral analysis techniques to ensure user privacy while delivering high-quality recommendations. Our initial findings indicate that while the accuracy of our GDPR-compliant RS is somewhat lower than that of traditional systems, it performs well, especially when there is a substantial volume of user interactions that mitigate the cold start problem. On average, more than 30 interactions are sufficient for compeling predictions. Our methodology enhances user privacy and trust, meets legal standards, and promotes ethical data handling practices.