Overpersonalization

Overpersonalization refers to the practice of tailoring content, recommendations, or experiences to an individual to an extent that exceeds reasonable expectations or consent. It results from collecting and integrating large amounts of personal data across devices and services, applying predictive models to infer preferences, intentions, or behaviors, and delivering highly individualized outputs in real time. While it can improve relevance and efficiency, overpersonalization can erode privacy, autonomy, and user agency.

Common domains include online advertising, social media feeds, news aggregators, e-commerce, and health or workplace applications. Examples include a streaming service repeatedly recommending similar titles after a single view; a retailer showing the same category ads across sites; push notifications that follow a user throughout the day; and cross-device profiling that makes inferences about sensitive attributes.

Risks and harms include privacy invasion, surveillance concerns, manipulation or exclusion, reinforcement of filter bubbles and bias, decision fatigue, reduced exposure to diverse viewpoints, and potential discrimination based on inferred traits. It can also increase dependency on algorithmic systems and raise security risks from data aggregation.

Mitigation and governance emphasize transparency, user consent, and control. Approaches include data minimization, purpose limitation, explainable recommendations, adjustable personalization levels, opt-out options, clear privacy notices, and regular audits for bias and safety. Regulatory frameworks such as privacy and data protection laws encourage opt-in consent, data subject rights, and restrictions on sensitive attribute inference.

Debate continues about balancing personalization benefits with privacy and autonomy. Effective policies aim to preserve user choice, foster accountability for algorithmic decisions, and reduce the risk of overreach while maintaining useful services.