A relevance feedback loop in information retrieval (IR) is a process where the system uses feedback from the user about the relevance of retrieved results to improve future searches. After an initial set of documents is retrieved, users can provide feedback (e.g., by marking documents as relevant or irrelevant). This feedback is then used to adjust the search model or query and retrieve better results.
For example, if a user finds some results helpful and others irrelevant, the system can modify the query by incorporating the terms from the relevant documents and excluding those from the irrelevant ones. This iterative process helps refine the search and tailor the results to the user's preferences.
The relevance feedback loop helps improve the performance of IR systems over time by making the search results more personalized and accurate. It is especially useful in scenarios where users have a specific but unclear information need, as it allows the system to learn from user interactions and better understand their intent.