An Effective and Efficient Algorithm for Ranking Web Documents via Genetic Programming

被引：1

作者：

Baeza-Yates, Ricardo ^{[1
]}

Cuzzocrea, Alfredo ^{[2
]}

Crea, Domenico ^{[3
]}

Lo Bianco, Giovanni ^{[3
]}

机构：

[1] Northeastern Univ Silicon Valley, San Jose, CA 95138 USA

[2] Univ Trieste, DIA Dept, Trieste, Italy

[3] Univ Calabria, DIMES Dept, Arcavacata Di Rende, Italy

来源：

SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING | 2019年

关键词：

Web Ranking; Learning Ranking Functions; Genetic Programming; Advanced Web Search Applications; DISCOVERY;

D O I：

10.1145/3297280.3297385

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We propose an effective and efficient algorithm for ranking web documents, called CombGenRank. This algorithm introduces a novel selection criterion in the classical genetic programming paradigm, which already proved to be effective for supporting web document ranking, called elitism. Extensive experimental results conducted on top of well-known web document collections confirm the benefits deriving from our proposed approach. This algorithm is motivated by search services based on cloud computing.

引用

页码：1065 / 1072

页数：8

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