Reinforcement learning (RL) improves information retrieval (IR) rankings by treating the search process as an optimization problem, where the system learns to maximize user satisfaction or engagement over time. In an IR context, RL algorithms adjust the ranking of search results based on continuous feedback from users, such as clicks or time spent on results.
For example, when a user interacts with the search results, the RL model evaluates the outcome and uses this feedback to adjust future rankings. The system learns which types of results are most relevant to users and adapts accordingly, ensuring that the ranking improves over time. This is particularly useful for dynamic, personalized search experiences.
By treating the search ranking process as a series of actions (selecting and ranking results), RL models can make more informed decisions and continually refine search results based on cumulative feedback, leading to a more relevant and personalized user experience.