计算机与现代化

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融合内容分析与标签拓展的社交标签推荐

  

  1. 上海交通大学软件学院,上海200240
  • 收稿日期:2015-11-30 出版日期:2016-05-24 发布日期:2016-05-25
  • 作者简介:俞崇伟(1990-),男,江西婺源人,上海交通大学软件学院硕士研究生,研究方向:数据挖掘,信息处理; 吴刚(1973-),男,副教授,研究方向:海量数据处理,自适应分布式计算。

Social Tagging Recommendation Based on Content Analysis and Tag Expansion

  1. School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2015-11-30 Online:2016-05-24 Published:2016-05-25

摘要:

近年来随着豆瓣读书、CiteULike等社交标签系统的快速发展,社交标签系统的推荐技术也逐渐成为研究热点。然而这类系统相比传统的推荐系统,增加了标签这个新的度量单位,导致了传统的协
同过滤推荐方式面临着推荐的标签质量不高、数据稀疏和冷启动、统一模型的适用性等问题,因此,基于传统的推荐方式无法取得很好的效果。本文提出一种融合内容分析与标签拓展的推荐方法TECA
(Tag Expansion and Content Analysis),并基于该方法为文本类型的资源实现标签和用户推荐。TECA通过分类细化资源模型,利用资源的语义主题分布推荐标签,并且利用用户标签拓展缓解数据稀疏
性。在CiteULike的数据上进行实验表明,TECA在标签推荐和用户推荐的效果上比协同过滤方式更优。

关键词: 社交标签系统, 标签推荐, 用户推荐, 标签拓展, 内容分析

Abstract:

With the rapid development of social tagging system such as Douban and CiteULike, the social tagging recommendation technology has become a hot research topic
recently. However, compared to traditional recommendation system, social tagging system has a new evaluation criterion and tag, which leads to the problems of low tag quality,
data sparsity, cold start and unified model applicability and so on. Hence, the traditional recommendation method can’t achieve satisfactory results. This paper proposed TECA
(Tag Expansion and Content Analysis), a method based on tag expansion and content analysis, and used TECA to implement tag and user recommendation. TECA can use classification
to refine the resource model, use semantic topic to recommend tags and use tag expansion to alleviate data sparsity. The experiment on CiteULike’s dataset shows TECA achieves
better tag and user recommendation performance than traditional collaborative filtering recommendation method.

Key words: social tagging system, tag recommendation, user recommendation, tag expansion, content analysis

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