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Machine learning models such as neural networks or decision trees can learn from data to make predictions or decisions, hence catering to individual user preferences based on data-driven insights.

A paradigm in which the context of a user (location, time of day, etc.) is taken into account by computing systems. It enhances personalization by delivering relevant content and services based on the user’s current context.

Websites that automatically personalize the content and structure in response to user interactions and preferences. They adapt in real-time to create a more relevant experience for each visitor.

The process of dividing a market of potential customers into groups, or segments, based on different characteristics. Personalization through segmentation tailors products and services to meet the needs of specific groups.

This technique uses item features to recommend additional items similar to what the user likes, based on their previous actions or explicit feedback. It's used for personalization by targeting content that matches a user's profile.

Content that changes based on the interests or past behavior of the viewer. It's used for personalization by providing a customized experience for each individual or segment.

The process of gathering user preferences to inform decision-making systems. It's used for personalization by explicitly asking users to provide input on their tastes and interests.

NLP allows computers to understand, interpret, and respond to human language in a valuable way. It's used for personalization by enabling more human-like interactions and understanding user intents.

These systems apply user-defined rules to data or inputs to produce outcomes, used in personalization to deliver content or decisions based on a set of predefined criteria and conditions.

A technique used in recommender systems where the system makes predictions about users' interests by collecting preferences from many users. It's used for personalization by recommending items based on similar users' tastes.

This is a method of comparing two versions of a webpage or app against each other to determine which one performs better. Personalization is achieved by tailoring experiences to the user based on the test results.