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http://dx.doi.org/10.6109/jkiice.2020.24.2.172

A Design of Similar Video Recommendation System using Extracted Words in Big Data Cluster  

Lee, Hyun-Sup (Department of application software Engineering, Dong-Eui University)
Kim, Jindeog (Department of Computer Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.2, 2020 , pp. 172-178 More about this Journal
Abstract
In order to recommend contents, the company generally uses collaborative filtering that takes into account both user preferences and video (item) similarities. Such services are primarily intended to facilitate user convenience by leveraging personal preferences such as user search keywords and viewing time. It will also be ranked around the keywords specified in the video. However, there is a limit to analyzing video similarities using limited keywords. In such cases, the problem becomes serious if the specified keyword does not properly reflect the item. In this paper, I would like to propose a system that identifies the characteristics of a video as it is by the system without human intervention, and analyzes and recommends similarities between videos. The proposed system analyzes similarities by taking into account all words (keywords) that have different meanings from training videos, and in such cases, the methods handled by big data clusters are applied because of the large scale of data and operations.
Keywords
Video; Word Extraction; Big Data Cluster; Similarity Analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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