The Future of Search Attention: Leveraging AI to Enhance PageRank's Influence

被引:0
|
作者
Amnoun, Hasnae [1 ]
Smaili, Naoual [2 ]
Barboucha, Hamza [1 ]
Kodad, Mohcine [3 ]
机构
[1] Mohammed I Univ, Lab Engn Sci, ENSAO, Oujda 60000, Morocco
[2] Natl Inst Stat & Appl Econ INSEA, SI2M Lab, BP 6217, Rabat 10112, Morocco
[3] Mohammed First Univ, MATSI Lab, ESTO, Oujda 60000, Morocco
关键词
Page rank; Artificial intelligence; Markov chain; Graph Neural Network;
D O I
10.1007/978-3-031-66850-0_14
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The introduction of PageRank revolutionized user search experiences, enabling companies to achieve prominence in Search Engine Results Pages (SERPs). The integration of Artificial Intelligence (AI) into search engines has shifted the focus from keyword-centric searches to a deeper understanding of user intent. This article presents a novel method for ranking business web pages in SERPs by enhancing the traditional PageRank algorithm with AI. We introduce a Graph Neural Network (GNN)-based approach that utilizes Markov chains to model the web as a graph of interconnected pages. This method incorporates user behavior and engagement metrics to improve the relevance and importance of web pages. By integrating AI, our approach aims to provide a more dynamic and user-centric ranking mechanism, transforming user interaction with online information and optimizing web presence for search engines.
引用
收藏
页码:125 / 132
页数:8
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