Graph-based rank aggregation: a deep-learning approach

Abstract

Purpose

This study aims to introduce a novel rank aggregation algorithm that leverages graph theory and deep-learning to improve the accuracy and relevance of aggregated rankings in metasearch scenarios, particularly when faced with inconsistent and low-quality rank lists. By strategically selecting a subset of base rankers, the algorithm enhances the quality of the aggregated ranking while using only a subset of base rankers.

Design/methodology/approach

The proposed algorithm leverages a graph-based model to represent the interrelationships between base rankers. By applying Spectral clustering, the algorithm identifies a subset of top-performing base rankers based on their retrieval effectiveness. These selected rankers are then integrated into a sequential deep-learning model to estimate relevance labels for query-document pairs.

Findings

Empirical evaluation on the MQ2007-agg and MQ2008-agg data sets demonstrates the substantial performance gains achieved by the proposed algorithm compared to baseline methods, with an average improvement of 8.7% in MAP and 11.9% in NDCG@1. The algorithm’s effectiveness can be attributed to its ability to effectively integrate diverse perspectives from base rankers and capture complex relationships within the data.

Originality/value

This research presents a novel approach to rank aggregation that integrates graph theory and deep-learning. The author proposes a graph-based model to select the most effective subset for metasearch applications by constructing a similarity graph of base rankers. This innovative method addresses the challenges posed by inconsistent and low-quality rank lists, offering a unique solution to the problem.

Citation

Keyhanipour, A.H. (2025), "Graph-based rank aggregation: a deep-learning approach", International Journal of Web Information Systems, Vol. 21 No. 1, pp. 54-76. https://doi.org/10.1108/IJWIS-09-2024-0278