personalisation

Personalisation refers to the process of tailoring products, services, or content to the individual user’s characteristics, preferences, or context. It relies on data about users and interactions to deliver more relevant experiences. In practice, it is often contrasted with customization, where users explicitly set preferences, whereas personalisation is driven by the system.

Techniques include data collection from explicit inputs (preferences, profile data) and implicit signals (clicks, time on page, purchase history). Profiling, audience segmentation, and recommender systems are common methods. Personalisation can be real-time, updating recommendations as a user interacts with a service, or batch-based, adjusting content periodically.

Applications include e-commerce product recommendations, personalised news or media feeds, adaptive software interfaces, customised educational content, and tailored healthcare or public services. It is also used in advertising and search results ranking.

Benefits include improved relevance, user satisfaction, engagement, and efficiency of task completion. Challenges include privacy and data protection concerns, consent management, potential biases, transparency, and the risk of creating filter bubbles or unintended discrimination. Implementation requires governance to balance usefulness with rights to privacy and autonomy.

Governance and ethics involve privacy-preserving techniques such as on-device personalization, federated learning, and differential privacy. Compliance with regulations (for example GDPR in the EU and similar laws elsewhere) and clear user controls are common requirements. Metrics for evaluation include engagement, retention, conversion, and user satisfaction, alongside measures of privacy impact and fairness.