论文部分内容阅读
个性化影片推荐服务是解决目前网络及家庭数字电视应用中影片资源迅速增长,用户“信息迷航”的有效方法。针对影片点播应用,给出了个性化影片推荐服务的体系结构、影片数据建模、用户兴趣偏好模型进行了研究,实现无需用户输入传统推荐方法所需相关个性兴趣信息即可返回与用户当前兴趣相关的影片推荐列表,提出了基于本体论的影片模型,并建立用户兴趣偏好模型,给出了对推荐过程中结合用户信息反馈对推荐结果进行自适应的调整算法。
Personalized video recommendation service is to solve the current network and home digital TV applications in the rapid growth of video resources, users “information trek ” effective method. For the video-on-demand application, the architecture of personalized video recommendation service, the video data modeling and the user preference preference model are given, which can be used to return the information related to the current interest of the user without inputting the relevant personal interest information required by the traditional recommendation method Related movie recommendation list, a movie model based on ontology is proposed, and a model of user interest preference is established. An algorithm for adjusting the recommendation result adaptively based on user information feedback in the recommendation process is given.