Title: A search ranking algorithm for web information retrieval

Authors: Shan Shan Zhi; Huan Huan Wang

Addresses: Big Data Institute, Henan Mechanical and Electrical Vocational College, Xinzheng, Henan 451192, China ' Smart City College, Henan Mechanical and Electrical Vocational College, Xinzheng, Henan 451192, China

Abstract: The development of the internet has seen an explosion in the amount of information, which has increased the scope of queries for users but greatly increased the difficulty of searching for valid information. In order to retrieve effective information faster, search ranking algorithms are needed to rank the retrieved information and return it to the user. This paper briefly introduced the RankNet algorithm among web information search ranking algorithms and optimised the loss function to improve its retrieval ranking performance. Simulation tests were carried out with Microsoft public data set MSLR-WEB30K. The improved RankNet algorithm was compared with the ranking support vector machine (SVM) algorithm and the traditional RankNet algorithm. The results showed that as the number of returned retrievals increased, the retrieval ranking performance of all three search ranking algorithms tended to decrease; under the same number of returned retrievals, the improved RankNet algorithm had the best performance.

Keywords: search ranking; rank learning; RankNet; pairing loss; support vector machine; SVM.

DOI: 10.1504/IJCNDS.2023.129225

International Journal of Communication Networks and Distributed Systems, 2023 Vol.29 No.2, pp.113 - 124

Received: 23 Dec 2021
Accepted: 17 Jan 2022
Published online: 01 Mar 2023 *

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