摘要
随着 Web 信息量的快速增长,个性化的 Web 信息推荐系统扮演着越来越重要的角色。目前,大多数 Web 信息推荐系统存在着个性化程度不高,对用户历史数据依赖性高,系统不具备开放性,用户偏好“走样”概率高的问题。针对这四个方面的问题,本文提出了一种新的、采用语义 Web 技术、基于 Web 社会网络的个性化 Web 信息推荐模型,详细分析了用户偏好的获取,Web 社会网络的生成以及待过滤 Web 信息的采集。并且,利用从实际中采集的真实数据进行了实验,证明了模型的可行性和有效性。
As fast growing of Web information, personal Web recommendation system would play more and more important role. Currently, however, most sueh recommendation systems mainly suffer from four problems. They separately are lacking personality, depending too much on users＇ history data, performing poor in decentralized environment, and sacrifieing users＇ interest profiles. In this paper, we propose a new personal Web recommendation model based on Web socialnetwork, using semantic Web technology. User＇ s interest capturing, Web social network formation, and filtering-needed Web information collection are thoroughly studied. Model＇ s feasibility and effectiveness are proved by experiments.
出处
《计算机科学》
CSCD
北大核心
2006年第4期185-187,193,共4页
Computer Science
关键词
个性化
Web信息推荐
资源描述框架
Weh社会网络
Personalization, Web recommendation, Resource description framework, Web social network
