Rank fusion methods are crucial in information retrieval and machine learning systems where different algorithms or sources produce distinct ranked outputs. These methods aggregate results to maximize relevance, mitigate individual biases, and enhance overall accuracy. In AI applications like meta-search engines, rank fusion ensures that outputs from various search engines are combined into a unified list that reflects diverse perspectives on relevance. Popular techniques include CombSUM, which sums the scores across rankings, and Borda count, which ranks items based on their positions in individual lists. Fusion methods often address challenges such as different score scales across sources and missing values in individual rankings, with applications extending beyond search engines to fields like recommendation systems, ensemble learning, and decision support systems.

Rank fusion gained traction in the late 1990s, coinciding with the rise of meta-search engines and the growing need to integrate results from various search algorithms. The development of systematic rank aggregation techniques paralleled the evolution of information retrieval models during this period.

Key contributions to rank fusion methods include early work by Gordon Cormack and Charles Clarke in the 1990s, particularly in the context of information retrieval and meta-search. Research groups focused on search engine technology and ensemble learning have continued to refine these methods.