针对专业领域问答系统中推荐专家回答不准确与不及时的问题,提出一种基于兴趣度、权威度、信誉度和最近活跃度的专家推荐混合模型。采用加权的LDA主题模型获得专家兴趣主题分布,采用基于主题的PageRank算法计算专家的权威度;根据专家回...针对专业领域问答系统中推荐专家回答不准确与不及时的问题,提出一种基于兴趣度、权威度、信誉度和最近活跃度的专家推荐混合模型。采用加权的LDA主题模型获得专家兴趣主题分布,采用基于主题的PageRank算法计算专家的权威度;根据专家回答问题的质量计算专家的信誉度,根据专家历史回答问题的时间获得专家的最近活跃度。给出用户问题的分析方法,采用混合模型推荐最适宜的问题服务专家。为了验证模型的可行性和有效性,使用Stack Over Flow真实数据集进行分析实验。实验结果表明该方法能够有效地提高新问题专家推荐的准确率。展开更多
In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personal...In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.展开更多
文摘针对专业领域问答系统中推荐专家回答不准确与不及时的问题,提出一种基于兴趣度、权威度、信誉度和最近活跃度的专家推荐混合模型。采用加权的LDA主题模型获得专家兴趣主题分布,采用基于主题的PageRank算法计算专家的权威度;根据专家回答问题的质量计算专家的信誉度,根据专家历史回答问题的时间获得专家的最近活跃度。给出用户问题的分析方法,采用混合模型推荐最适宜的问题服务专家。为了验证模型的可行性和有效性,使用Stack Over Flow真实数据集进行分析实验。实验结果表明该方法能够有效地提高新问题专家推荐的准确率。
基金The National Natural Science Foundation of China(No60573090,60673139)
文摘In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.
