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The Model of Personalized Information Retrieval Recommendation Based on LDA and Social Network Centrality Analysis
Received date: 2015-05-17
Revised date: 2015-06-20
Online published: 2015-07-05
[Purpose/significance] In order to solve the retrieval problem of postgraduates, this paper proposes a personalized retrieval recommendation model based on LDA and the social network centrality analysis. [Method/process] Firstly, this model takes the discipline and specialty of postgraduate as the personalized characteristics, and selects the relevant literature data source according it. Secondly, this model uses the LDA to identify the topic content, and completes the display of the comprehensive knowledge. Thirdly, this model takes the social network centrality analysis in the keyword-topic co-occurrence network according to the retrieval word, and completes the relevant knowledge recommendation. [Result/conclusion] Experimental results show that this model can solve the three retrieval problems of postgraduates, such as the personalized features, the comprehensive knowledge display and the relevant knowledge recommendation. Its validity is verified to some extent.
Ye Chunlei , Xing Yanli . The Model of Personalized Information Retrieval Recommendation Based on LDA and Social Network Centrality Analysis[J]. Library and Information Service, 2015 , 59(13) : 104 -110 . DOI: 10.13266/j.issn.0252-3116.2015.13.015
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