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http://dx.doi.org/10.9717/kmms.2019.22.2.250

A Method on Associated Document Recommendation with Word Correlation Weights  

Kim, Seonmi (Dept. of Software Convergence Engineering Chosun University)
Na, InSeop (SW Convergence Education Institute, Chosun University)
Shin, Juhyun (Dept. of ICT Convergence, Chosun University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.2, 2019 , pp. 250-259 More about this Journal
Abstract
Big data processing technology and artificial intelligence (AI) are increasingly attracting attention. Natural language processing is an important research area of artificial intelligence. In this paper, we use Korean news articles to extract topic distributions in documents and word distribution vectors in topics through LDA-based Topic Modeling. Then, we use Word2vec to vector words, and generate a weight matrix to derive the relevance SCORE considering the semantic relationship between the words. We propose a way to recommend documents in order of high score.
Keywords
Big Data; Datamining; LDA; Word2vec; Topic Modeling; Information Retrieval; Document Recommendation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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