关键词: 深度学习, 在线视频服务, 热度预测, 深度信念网络, 受限玻尔兹曼机

Abstract: Concerning the issue of traditional prediction model, this paper proposes a Deep Belief Networks（DBNs）based approach to predict the popularity of online videos. By modeling users’ attention with the information extracted from social network and search engine, it studies its feature selection method and the optimization of DBN’s parameters. Based on the data collected from one large-scale online video service provider, it implements experiments to evaluate the proposed approaches. The results show that DBN-based prediction obtains the highest performance up to 79.47%（for domestic TV drama）and 65. 33%（for foreign TV drama）. The prediction strategy can help provide decision-making information for risk assessment, publicity affair, and investment of online videos.

Key words: deep learning, online video service, popularity prediction, deep belief network, restricted Boltzmann machine