Comparing information retrieval (IR) systems involves evaluating their performance based on several metrics, such as relevance, efficiency, and accuracy. Key metrics used for comparison include precision, recall, F1 score, and Mean Average Precision (MAP). These metrics assess how well the IR system retrieves relevant documents in response to a query.
Additionally, systems can be compared in terms of their ability to handle large-scale datasets, their robustness in dealing with noisy or ambiguous queries, and their adaptability to evolving user needs. Benchmark datasets and standardized evaluation frameworks, such as TREC (Text REtrieval Conference) or CLEF (Conference and Labs of the Evaluation Forum), are commonly used for objective comparisons.
User-centric factors such as system speed (latency), scalability, and the ability to provide personalized search results also play a significant role in the overall comparison of IR systems.