This study explores users’ perceptions of integrating a personal data store to enhance personalized recommendations within a streaming service. Using a research-through-design approach and guided by Human Data Interaction principles (legibility, agency, and negotiability), we developed an enhanced streaming service prototype. This prototype was evaluated by experts (n=5), refined, and then used in two focus groups (n=19) to gauge participants’ reactions to the personal data store integration and their willingness to share different data types for enhanced personalized streaming recommendations. The focus groups revealed mixed reactions to the personal data store, with users weighing curiosity against concerns. However, many of the implemented data transparency and control features helped to mitigate these doubts. By linking our findings to existing literature, we developed a set of design recommendations to help businesses and guide future research in building personal data store applications, further advancing the field of Human Data Interaction.
https://dl.acm.org/doi/10.1145/3706598.3713308
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)