ProbFuse: A Probabilistic Approach to Data Fusion

TL;DR

ProbFuse introduces a probabilistic data fusion method that leverages training query performance to improve retrieval effectiveness over traditional algorithms like CombMNZ.

Contribution

It presents a novel probabilistic approach to data fusion that uses training data to estimate relevance probabilities for better result merging.

Findings

ProbFuse outperforms CombMNZ in retrieval effectiveness.

Training-based probability estimation improves fusion results.

Experimental validation on TREC data supports the approach.

Abstract

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.