面向视频服务的个性化推荐系统能够有效解决其中的“信息过载”问题。然而，该系统本身却存在着数据维度过高、用户评分数据稀疏以及冷启动等问题。为解决上述问题，本项目在协同优化超高维用户稀疏评分矩阵和深入挖掘用户群体的收视行为特征基础上，提出了面向网络用户群体的个性化的视频协同推荐方案。拟研究内容为：通过采用非负矩阵分解和深度学习理论的方法，有效地解决数据维度过高且用户评分数据稀疏的问题，达到建立超高维的用户稀疏评分矩阵优化模型的目的；通过采用隐含狄利克雷分布与模糊聚类理论的方法，解决“冷启动”问题，达到建立用户的隐含收视行为特征模型的目的；通过对优化后的用户评分矩阵、用户隐含特征信息和项目隐含特征信息的挖掘，建立精准度高、扩展性强的个性化视频协同推荐模型。该研究成果不但可进一步促进个性化推荐理论在视频服务中的应用，而且对于其他个性化推荐系统研究均有重要借鉴意义，并有力推进该产业的进一步发展。

Personalized recommender system can effectively solve the "information overload" problem in video service system. However, the performance of the system is subjected to the ultra-high data dimensions, sparse users’ ratings data as well as cold start problems. In order to provide a better solution to the above problems, the project optimizes the ultra-high users’ sparse ratings matrix collaboratively and deeply mines the viewing behavior characteristics of the network user groups, and then this project introduces a users’ group-oriented scheme for the personalized video collaborative recommendations. The specific research contents are as follows: The problem of the ultra-high dimensional data and sparse users’ ratings matrix will be solved efficiently by adopting the Non-Negative Matrix Factorization and Deep Learning theory, and hence, the ultra-high dimensional sparse users’ ratings matrix optimization model is constructed. The cold start problem will be solved successfully based on the Latent Dirichlet Allocation and Fuzzy Clustering theory, and then the viewing behavior characteristics model of the network users’ group is formed. The personalized video collaborative recommendation model with high precision and strong scalability feature will be established by fully taking advantages of the optimized users’ ratings matrix, users’ latent auxiliary features, and the items’ latent auxiliary features. The research achievements not only further promote the application of the personalized recommendation theory in the video services but also have important reference for researching the other personalized recommendation systems, and push ahead them with the further developments finally.