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dc.contributor.advisor林琛
dc.contributor.author王菁菁
dc.date.accessioned2017-06-20T08:45:31Z
dc.date.available2017-06-20T08:45:31Z
dc.date.issued2016-12-23
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/134813
dc.description.abstract近年来,观点摘要技术为世界各地的消费者带来了极大的便利。从大量的在线商品评论中,观点摘要技术自动为给定商品的大众观点生成摘要。然而,当前的观点摘要系统为每个商品所提供的摘要通常是静态、粗粒度的,这样的摘要在处理高度动态和个性化的用户偏好时具有很大的局限性。因此,在用户评估候选商品的阶段,这种摘要无法为其提供所需要的有效的指导意见。 在本文中,我们通过生成个性化的多商品摘要为消费者提供决策支持。本文的目标是生成简洁的商品动态摘要,它可以体现出用户所喜爱的特征的重要信息,同时能够兼顾不同商品之间的差异性。 首先,为了使得生成的摘要满足以下的三个特征:高度精简性、集中覆盖性、差异性,本文将个性化...
dc.description.abstractNowadays, opinion summarization technologies have brought conveniences to consumers worldwide by making an abstract of the public opinions on a given product automatically from massive online product reviews. However, state-of-the-art opinion summarization systems, which provide a static, coarse grained summary for each product, have their limits in handling highly dynamic and personalized user pr...
dc.language.isozh_CN
dc.relation.urihttp://210.34.4.28/opac/openlink.php?strText=53826&doctype=ALL&strSearchType=callno
dc.source.urihttp://210.34.4.13:8080/lunwen/detail.asp?serial=54898
dc.subject个性化摘要
dc.subject观点挖掘
dc.subject特征层次结构
dc.subjectPersonalized Summarization
dc.subjectOpinion Mining
dc.subjectAspect Hierarchy
dc.title基于在线评论的个性化多产品摘要算法的研究
dc.title.alternativePersonalized Multi-product Summarization based on Online Reviews
dc.typethesis
dc.date.replied2016-05-11
dc.description.note学位:工学硕士
dc.description.note院系专业:信息科学与技术学院_计算机科学与技术
dc.description.note学号:23020131153171


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