Clip recommendation based on topic similarity

Wonjoo Park, Jeong-Woo Son, Sang-Yun Lee, Sun-Joong Kim
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Abstract

We propose a clip recommendation technology based on topic similarity. Topics of a clip can represent semantics of each contents. When the topic distributions for clips are similar, it means they are alike. In this paper, we propose a system to learn topic distributions for broadcast contents and link clips based on topics similarity of each clip. The higher the similarity is among the clips, the higher the semantic is among them. This system can be adopted clip recommendation with audiences viewing history and their interest.
基于主题相似度的剪辑推荐
提出了一种基于主题相似度的视频片段推荐技术。一个片段的主题可以表示每个内容的语义。当剪辑的主题分布相似时,就意味着它们是相似的。在本文中,我们提出了一个基于每个片段的主题相似度来学习广播内容和链接片段的主题分布的系统。片段之间的相似度越高,片段之间的语义度越高。该系统可以根据观众的观看历史和兴趣进行视频推荐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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