Machine learning (ML) improves information retrieval (IR) by enabling systems to learn from data and optimize their performance over time. ML models analyze past search interactions to identify patterns and preferences, which can then be used to predict more relevant search results in the future.
For instance, ML algorithms can be used to improve ranking algorithms by learning from user clicks and feedback. When users interact with search results, ML models can determine which results were most helpful and adjust rankings accordingly. This process allows IR systems to become smarter and more accurate over time.
Machine learning also aids in automating tasks like query understanding, relevance estimation, and content classification, reducing the need for manual intervention. By continuously learning from vast amounts of data, machine learning-powered IR systems can adapt to changes in user behavior and improve search relevance, making them more effective in dynamic environments.