ProbFuse: A Probabilistic Approach to Data Fusion
This work addresses the need for more effective data fusion techniques in information retrieval, offering a novel probabilistic approach that shows superior performance over existing methods.
The paper tackles the problem of improving retrieval effectiveness by combining results from multiple search systems, introducing ProbFuse, a probabilistic data fusion method that outperforms the popular CombMNZ algorithm in experiments on the TREC ad hoc collection.
Data fusion is the combination of the results of independent searches on a document collection into one single output result set. It has been shown in the past that this can greatly improve retrieval effectiveness over that of the individual results. This paper presents probFuse, a probabilistic approach to data fusion. ProbFuse assumes that the performance of the individual input systems on a number of training queries is indicative of their future performance. The fused result set is based on probabilities of relevance calculated during this training process. Retrieval experiments using data from the TREC ad hoc collection demonstrate that probFuse achieves results superior to that of the popular CombMNZ fusion algorithm.