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.

Amir Hosein Keyhanipour

International Journal of Web Information Systems

The Global Information Infrastructure is a daily reality. In spite of the many applications in all domains of our societies: e-business, e-commerce, e-learning, e-science, and e-government, for instance, and in spite of the tremendous advances by engineers and scientists, the seamless development of Web information systems and services remains a major challenge. The journal examines how current shared vision for the future is one of semantically-rich information and service oriented architecture for global information systems. This vision is at the convergence of progress in technologies such as XML, Web services, RDF, OWL, of multimedia, multimodal, and multilingual information retrieval, and of distributed, mobile and ubiquitous computing.    Topicality  While the International Journal of Web Information Systems covers a broad range of topics, the journal welcomes papers that provide a perspective on all aspects of Web information systems: Web semantics and Web dynamics, Web mining and searching, Web databases and Web data integration, Web-based commerce and e-business, Web collaboration and distributed computing, Internet computing and networks, performance of Web applications, and Web multimedia services and Web-based education.

The International Journal of Web Information Systems focuses on the reality of the Global Information Infrastructure. Despite numerous applications across various domains such as e-business, e-commerce, e-learning, e-science, and e-government, and significant advancements made by engineers and scientists, the seamless development of Web information systems and services remains a major challenge. This journal explores the shared vision for the future, which emphasizes semantically-rich information and service-oriented architecture for global information systems. This vision converges with advancements in technologies such as XML, Web services, RDF, OWL, as well as multimedia, multimodal, and multilingual information retrieval, and distributed, mobile, and ubiquitous computing. While the journal covers a broad range of topics, it welcomes papers that provide insights into all aspects of Web information systems, including Web semantics and dynamics, Web mining and searching, Web databases and data integration, Web-based commerce and e-business, Web collaboration and distributed computing, Internet computing and networks, performance of Web applications, and Web multimedia services and education.

《International Journal of Web Information Systems》致力于探讨全球信息基础设施的现实。尽管在电子商务、电子学习、电子科学和电子政府等各个领域有许多应用，并且工程师和科学家们取得了巨大的进展，但无缝开发Web信息系统和服务仍然是一个重大挑战。本期刊研究当前对未来的共同愿景，即为全球信息系统提供语义丰富的信息和面向服务的架构。这一愿景与XML、Web服务、RDF、OWL等技术的进步，以及多媒体、多模态和多语言信息检索、分布式、移动和无处不在的计算相交汇。期刊涵盖广泛的主题，欢迎提供关于Web信息系统各个方面的论文，包括Web语义和Web动态、Web挖掘和搜索、Web数据库和Web数据集成、基于Web的商业和电子商务、Web协作和分布式计算、互联网计算和网络、Web应用性能，以及Web多媒体服务和基于Web的教育。

Graph-based rank aggregation: a deep-learning approach

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2