在线媒体快速发展,为用户带来丰富多彩信息的同时,用户的参与也给在线媒体本身带来巨大的经济利益.因此,如何通过精确预测用户的偏好以增加在线媒体点击,成为一个学术界和工业界均关注的问题.现有的预测方法主要是借助用户个人信息和历史行为来预测用户行为,然而此类方法没有考虑媒体本身缺乏用户信息造成无法预测的问题.随着社交网络的发展,在线媒体与服务运营商间的兼并或合作的增多,支持用户通过单一账户使用多个媒体网络服务的情况越来越常见,这就为基于用户在社交网络中的资料预测用户在其他媒体中的喜好提供海量可信的基础数据.该文基于社交网络Google+和视频媒体YouTube的数据,首先证明用户在YouTube偏好具有高度的集聚性,并提出用户在社交网络中偏好与其在线媒体点击行为具有关联性,基于这种关联性,该文使用社交网络用户信息预测用户在在线媒体中的点播行为.实验结果显示,使用社交网络用户信息可以有效预测用户偏好,预测准确率比仅使用媒体本身信息提高了17%,而且能满足用户个性化需求.%Online media experienced a huge improvement in the last few years ,causing the user preference prediction a substantial issue so as to increase the user's clicks .The data sparsity in both the user information and the historical behavior records deteriorates many well-known predication system .Based on data of Google users ,this paper re-veals that the user's "likes" on online media are converged .In particular ,we detect the correlation between the user"likes" on online media and his profile in social network ,suggesting that the user profile in social network can pre-dict user's likes on online media .Based on the correlation ,we apply the user's social network description to predict his "likes" on online media ,resulting more than 17% improvement in precision compared with algorithms using only the user information from online media .
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