Learning to rank through graph-based feature fusion using fuzzy integral operators

Abstract

Accurately ranking search results based on user query relevance is a complex, multi-dimensional challenge in information retrieval systems, inherently subject to ambiguity and uncertainty. This inherent complexity stems from the ambiguity and uncertainty surrounding relevance judgments. Factors like imprecise user queries, expert disagreements on relevance, and complex relationships between features of documents and queries all contribute to this. Traditional learning-to-rank algorithms often struggle to handle these uncertainties. This paper proposes a novel approach that leverages Sugeno and Choquet fuzzy integrals to model the uncertainty of features and their interactions. This allows our algorithm to make more nuanced ranking decisions. The proposed approach is extensively evaluated on major benchmark datasets like MSLR-Web10K, Istella LETOR, and WCL2R, demonstrating its effectiveness in outperforming baseline methods across standard criteria such as P@n, MAP, and NDCG@n. Notably, the proposed algorithm ranks top results, which are most crucial for user satisfaction. This practical improvement can benefit web search engines by providing users with more relevant information at the top of their search results.