随着微博网络的盛行,越来越多的微博信息困扰用户无法快速定位自己感兴趣的博文。为了解决微博信息过载问题,信息过滤、推荐和搜索等技术被应用于微博研究中。该文提出了一个综合信任模型、社会网络关系分析的综合推荐模型,应用LDA主题模型及矩阵分解技术推断微博的主题分布和用户的兴趣取向,实现微博的个性化推荐。通过实验验证,该方法能十分有效地解决个性化博文推荐问题。
Abstract
Due to the rapid growth of microblogs, bloggers are facing difficulties in locating the microblogs they are interested. To deal with this information overload, various approaches including messages filtering, recommendation and searching have been investigated. Focusing on recommending bloggers or microblog posts by the trust model and the social relationship, this paper applies LDA topic model and Matrix Factorization to infer the topic distribution of microblogs and the user interest. According to the experimental results, the proposed method can effectively solve the personalized recommendation of microblog.
关键词
信任度 /
社会网络 /
矩阵分解 /
微博 /
LDA
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Key words
trust /
social networks /
matrix factorization /
blog /
LDA
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参考文献
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基金
国家自然科学基金(61403156,61403155);江苏省科技项目(BN2016065);江苏省海资院开放课题(JSIMR201403);连云港市科技计划项目(SH1507,CXY1530,CK1503,NYYQ1620);淮海工学院自然科学基金资助(Z2014007,Z2014012)
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