SVD技术在视频推荐系统中的应用

Menghan Yan, Wenqian Shang, Zhenzhong Li
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引用次数: 6

摘要

评估什么样的话题对视频制作者来说是有价值的,并给他们带来灵感的最直接途径是寻找特定群体目前关注的话题。我们可以从社交网络平台、大型视频网站、搜索引擎中获取海量的用户信息,然后结合业务需求,利用这些数据制作出更实用的作品。针对目前存在的可扩展性差、稀疏性问题和测试数据量大的缺点,应用奇异值分解方法(SVD)实现了测试集的未知预测分数函数。仿真结果表明,该方法有效地提高了可扩展性、稀疏性和计算效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of SVD technology in video recommendation system
The most direct access to evaluate what kinds of topics are valuable for video producers, and bring them inspiration is to seek subjects which specific groups concern currently. We can obtain massive user information from social networking platforms, large video sites and search engines, and then exploit the data to produce more practical works with the combination of business requirements. In views of the existing disadvantages of inferior scalability, sparsity problem and huge volume test data, the application of Singular Value Decomposition Method(SVD) actualize the unknown prediction score function of set of tests. The simulation results show that scalability, sparsity and omputational efficiency improved effectively.
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