摘要
随着移动互联网和大数据的快速发展,网络中产生了海量的短视频数据。为高效地向用户推荐感兴趣的短视频,需要用到推荐系统,解决短视频信息过载问题。为了提高短视频推荐系统准确率,提出一种基于Faster R-CNN深度学习网络的视频内容检测方案应用在短视频推荐场景,提取短视频内容特征。分析常用的短视频推荐算法的优劣及推荐效果评价指标,结合复杂应用场景,使用混合推荐算法能够有效地提高个性化推荐系统的推荐效果。
With the rapid development of mobile Internet and big data,massive amounts of short video data have been generated on the Internet.In order to efficiently recommend short videos of interest to users,a recommendation system is needed to solve the problem of short video information overload.In order to improve the accuracy of the short video recommendation system,a video content detection scheme based on the Faster R-CNN deep learning network is proposed and applied in the short video recommendation scene to extract short video content features.Analyzing the pros and cons of commonly used short video recommendation algorithms and the evaluation indicators of recommendation effect,combined with complex application scenarios,using hybrid recommendation algorithms can effectively improve the recommendation effect of the personalized recommendation system.
作者
汤志鹏
TANG Zhipeng(School of Information Technology,Guangdong Polytechnic College,Zhaoqing Guangdong 526100,China)
出处
《信息与电脑》
2021年第13期39-42,共4页
Information & Computer
基金
广东理工学院质量工程项目“‘互联网+’时代下《移动互联开发》课程教学改革与研究”(项目编号:JXGG202058)。
关键词
短视频
内容检测
推荐算法
short video
content detection
recommendation algorithm
