Personalization in information retrieval (IR) systems tailors search results based on an individual user's preferences, behavior, and past interactions. By analyzing user data, such as previous queries, clicks, and feedback, the system can learn which types of content are most relevant to that user.
For example, in a shopping recommendation system, personalization ensures that the user sees products similar to those they have previously viewed or purchased. The system uses algorithms to analyze patterns in user behavior and adjust recommendations accordingly. Personalization can also incorporate contextual factors like time, location, or device to further refine search results.
Personalized IR systems improve user satisfaction by providing more relevant and targeted content, reducing the effort required to find what the user is looking for. These systems are often powered by machine learning, which helps continuously refine and adapt recommendations based on the user's evolving preferences.