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Privacy Concerns in Online Recommender Systems: Influences of Control and User Data Input
Bo Zhang and Na Wang, Pennsylvania State University and Samsung Research America; Hongxia Jin, Samsung Research America
Recommender systems (e.g., Amazon.com) provide users with tailored products and services, which have the potential to induce user privacy concerns. Although system designers have been actively developing algorithms to introduce user control mechanisms, it remains unclear whether such control is effective in alleviating privacy concerns. It also is unclear how data type affects this relationship. To determine the psychological mechanisms of user privacy concerns in a recommender system, we conducted a scenario-based online experiment (N = 385). Users’ privacy concerns were measured in relation to different data input (explicit vs. implicit) and control (present vs. absent) scenarios. Results show that a control mechanism can effectively reduce users’ concerns over implicit user data input (i.e., purchase history) but not over explicit user data input (i.e., product ratings). We also demonstrate that control can influence privacy concerns via users’ perceived value of disclosure. These findings question the effectiveness of user control mechanisms in recommender systems with explicit data input. Additionally, our item categorization provides a reference for future personalized recommendations and future analyses.
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