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Linear combination is a potent data fusion method in information retrievaltasks, thanks to its ability to adjust weights for diverse scenarios. However,achieving optimal weight training has traditionally required manual relevancejudgments on a large percentage of documents, a labor-intensive and expensiveprocess. In this study, we investigate the feasibility of obtainingnear-optimal weights using a mere 20\%-50\% of relevant documents. Throughexperiments on four TREC datasets, we find that weights trained with multiplelinear regression using this reduced set closely rival those obtained withTREC's official "qrels." Our findings unlock the potential for more efficientand affordable data fusion, empowering researchers and practitioners to reapits full benefits with significantly less effort.

Streamlined Data Fusion: Unleashing the Power of Linear Combination with Minimal Relevance Judgments