Relevance drift occurs when the effectiveness of an information retrieval (IR) system deteriorates over time, often due to changes in user behavior, content, or underlying algorithms. To address relevance drift, IR systems can incorporate continuous learning mechanisms, such as re-training models or updating ranking algorithms to adapt to new data.
One method is to monitor user interaction with the search results and adjust the model based on feedback such as clicks, time spent on pages, or user ratings. Another approach is to introduce adaptive ranking models that account for changing trends or preferences in search queries.
Additionally, feedback loops, where relevant documents are continuously added to training data, can help mitigate relevance drift and maintain the quality of search results.