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标签标志着在web2.0时代用户从被动的消费者变为主动的信息创造者,用户可以自由的在网络上创建和使用代表自己意愿的任意标签。如何基于标签进行个性化的信息推荐是目前许多学者关注的一个问题,本文共总结了三类基于标签的个性化信息推荐模型:基于图论、基于矩阵和基于主题的模型,然后提出现有模型的缺陷及未来急需解决的问题。
Tags in the web2.0 era marked the transition from passive consumers to active information creators, users are free to create and use on the network to represent any of their own tags. How to recommend personalized information based on tags is a problem that many scholars pay close attention to at present. This paper summarizes three types of tag-based personalized information recommendation models: graph theory, matrix-based and topic-based models, and then presents the existing models The shortcomings and the urgent need to solve the problem.