Web 2.0时代,社会标签是信息资源组织的一种重要方式。标签推荐能够有效的帮助用户收集、定位、查找和共享在线资源。以往的标签推荐算法只是基于一种文本信息,比如基于电影的简介文本来进行标签推荐。但是实际上电影往往存在多种文本信息,比如同时存在摘要信息和评论信息,不同类型的信息能够反映电影的不同方面的属性,因此为了提高电影标签推荐的准确率和有效性,我们同时根据电影的简介和短评进行电影标签自动推荐,并使用多种方法融合基于不同类型文本的标签推荐的结果,实验证明,使用不同类型信息进行标签推荐能够比单一使用一种文本信息进行标签推荐有很大的提升。
Abstract
Social tags are important styles of information organizing on the Web 2.0 era. Tag recommendation can help users collect, search and share online resources effectively. The previous approaches are focused on using single types of textual information, e.g. summary of a movie. But in practice there are various types of textual information that can be used for tag recommendation. For example, a movie contains both summary and comment information. Different types of information reflect different aspects of the movie. Thus we propose a novel approach to combine both summary and comment information to recommend tags. Furthermore, we use different ensemble learning approaches to incorporate the above information. The experimental results show that our proposed approach using different types of information outperform using single types of textual information in the tag recommendation tasks.
Key words natural language processing; social tags; ensemble learning
关键词
自然语言处理 /
社会标签 /
社会关系网络 /
分类器融合
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Key words
natural language processing /
social tags /
ensemble learning
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脚注
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基金
国家自然科学基金(61272260,61331011);江苏省高校自然科学基金(11KJA520003)
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