计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 103-107.doi: 10.11896/jsjkx.190700177

• 计算机图形学&多媒体 • 上一篇    下一篇

一种面向多维特征分析过滤的视频推荐算法

赵楠, 皮文超, 许长桥   

  1. 北京邮电大学网络技术研究院 北京100876
  • 收稿日期:2019-07-25 出版日期:2020-04-15 发布日期:2020-04-15
  • 通讯作者: 许长桥(cqxu@bupt.edu.cn)

Video Recommendation Algorithm for Multidimensional Feature Analysis and Filtering

ZHAO Nan, PI Wen-chao, XU Chang-qiao   

  1. Institute of Network Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Received:2019-07-25 Online:2020-04-15 Published:2020-04-15
  • Contact: XU Chang-qiao,born in 1977,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include Mobile Internet,multimedia communications,cloud computing and big data,content distribution and transmission.
  • About author:ZHAO Nan,born in 1996,postgraduate.His main research interests include multimedia communication,cloud computing and big data

摘要: 近年来,抖音、快手、微视等短视频APP取得了巨大成功,用户拍摄并上传到APP平台上的视频数量暴增。在这种信息过载的环境下,为用户挖掘并推荐其感兴趣的视频成为了视频发布平台面临的难题,因此为这些平台设计高效的视频推荐算法显得尤其重要。文中针对媒体大数据挖掘和推荐领域的数据集稀疏性高和规模巨大的问题,提出一种面向多维特征分析过滤的视频推荐算法。首先,从用户行为和视频标签等多个维度对视频进行特征提取,然后进行相似性分析,加权计算视频相似度,从而获取相似视频候选集,并对相似视频候选集进行过滤,再通过排序选择评分最高的若干个视频推荐给用户。最后,基于MovieLens公开数据集,使用python3语言实现了文中提出的视频推荐算法。在数据集上进行的大量实验表明,相比传统的协同过滤算法,文中提出的面向多维特征分析过滤的视频推荐算法将推荐结果的准确率提升了6%,召回率提升了4%,覆盖率提升了18%。实验数据充分说明,从多个维度考虑视频之间的相似性,并配合大规模矩阵分解技术,在一定程度上缓解了数据集稀疏性高、数据量巨大的难题,从而有效地提高了推荐结果的准确性、召回率和覆盖率。

关键词: 视频推荐, 特征提取, 相似性分析, 协同过滤, 稀疏性

Abstract: In recent years,short video apps such as TikTok,Kwai,and WeiShi have achieved great success,and the number of videos taken by users and uploaded to the APP platform has skyrocketed.In this environment of information overload,mining and recommending videos of interest to users has become a problem faced by video publishing platforms.Therefore,it is particularly important to design efficient video recommendation algorithms for these platforms.Aiming at the problem of high sparseness and huge scale of datasets in the field of media big data mining and recommendation,a video recommendation algorithm for multidimensional feature analysis and filtering is proposed.First,feature extraction is performed on videos from multiple dimensions such as user behavior and video tags.Then,similarity analysis is performed to calculate the video similarity by weighting to obtain similar video candidate sets,the similar video candidate sets are filtered,and then several videos selected by ranking the highest rated videos are recommended to users.Finally,based on the MovieLens public data set,the video recommendation algorithm proposed in this paper is implemented by using python3 programming language.A large number of experiments on the data set show that compared with the traditional collaborative filtering algorithm,the video recommendation algorithm for multidimensional feature analysis and filtering proposed in the paper improves the accuracy of the recommendation results by 6%,the recall rate by 4%,and the coverage rate by 18%.The experimental data fully demonstrates that considering the similarity between videos from multiple dimensions,combined with large-scale matrix factorization technology,the problems of high sparseness and huge data volume of the data set are alleviated to some extent,thereby effectively improving the recommendation results accuracy,recall,and coverage.

Key words: Video recommendation, Feature extraction, Similarity analysis, Collaborative filtering, Sparsity

中图分类号: 

  • TP399
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