文献类型：期刊文献

英文题名：Research on joint ranking recommendation model based on Markov chain

作者：Jia, Hailong[1];Yang, Jie[2]

第一作者：贾海龙

通讯作者：Yang, J[1]

机构：[1]Xinxiang Univ, Xinxiang, Henan, Peoples R China;[2]Wuhan Univ Technol, Wuhan 430063, Peoples R China

第一机构：新乡学院

通讯机构：[1]corresponding author), Wuhan Univ Technol, Wuhan 430063, Peoples R China.

年份：2020

卷号：32

期号：6

外文期刊名：CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000515571000001)】；

基金：National Natural Science Foundation of China (NSFC), Grant/Award Number: 51879211

语种：英文

外文关键词：joint ranking; Markov chain; search engine

摘要：In this paper, a supervised learning framework with strong expansibility is first established for search engine joint ranking problem. It can transform existing algorithms into corresponding learning algorithms, and design new algorithms under this framework. Second, with Markov chain model as the core algorithm, this paper combines the ranking results of three main factors, including content relevance, hyperlink prediction, and query click behavior, and transforms the joint problem of ranking results into a positive semi-definite programming problem, and deduces the detailed process of solving the problem. Finally, this paper analyzes the rationality and efficiency of the joint ranking recommendation model based on Markov chain by setting the weight coefficient through experimental data.