Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： For multi-attribute group decision-making(MAGDM) problem, this study use Pythagorean fuzzy linguistic numbers as input information which can make decision maker evaluate the options effectively with more flexible values. First, Pythagorean fuzzy linguistic distance measure based on the distance measure for Pythagorean fuzzy sets and linguistic sets was created and its properties were also given. Then, by using this distance measure in power average operator , the Pythagorean fuzzy linguistic power weighted average (PFLPWA) operator was created to aggregate a collection of Pythagorean fuzzy linguistic numbers (PFLNs) , and the differences of the decision makers were considered in this aggregation process. Finally, a group decision making method on the basis of PFLPWA operator was built and a numerical example was given to demonstrate its effectiveness and feasibility;
Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-08 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】社交网络环境下的用户兴趣建模是好友推荐、精准营销的关键, 利用微博用户分享的图像, 提出一种基于图像语义的用户兴趣建模方法, 旨在更加准确地预测用户的真实兴趣。【方法】在获取新浪微博用户图像数据的基础上, 使用图像的高层语义表达用户兴趣特征, 基于这些特征使用SVM 训练得到图像语义分类器进行预测。【结果】实验结果表明, 本文建立的模型能够较为准确地预测用户真实兴趣, 169 位用户分类的准确率达到97.38%, 召回率为98.92%, F 值为98.14%。【局限】由于实验图像数据集有限, 未能完整地覆盖用户所有的兴趣类别。【结论】该模型能够基于用户分享的图像较为准确地预测用户兴趣, 表明了图像高层语义的有效性, 同时为图像高层语义应用研究提供了一定的理论和技术基础。
Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-08 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】针对不同查询专指度语句的检索效果进行全面分析, 为改善搜索引擎性能、提高用户检索体验提供借鉴。【方法】基于TREC Web Track 查询语句, 人工构建查询专指度标注集, 选用语言模型狄利克雷平滑、语言模型线性插值平滑和BM25 三种模型, 以常用的信息检索评价指标为基准, 探讨查询专指度强弱对检索效果在不同层次上的影响。【结果】在最靠前的几条检索结果中, 强弱专指度查询语句的检索效果差异最大, 强专指度的检索效果要明显好于弱专指度。【局限】仅在TREC 数据集上进行实验测试, 还需在其他数据集上进一步检验。【结论】搜索引擎在专指度这一维度下, 应重点关注最靠前的几条检索结果的准确性, 以此为切入点改善检索模型。
Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-08 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】建设面向知识层次的标准文献服务系统, 推进标准文献信息服务的知识化进程。【应用背景】标 准文献知识服务系统能够对标准文献中的知识单元进行语义抽取, 依据标准文献知识之间的关联关系进行有效 组织, 并为用户提供面向知识层次的标准文献信息服务。【方法】采用光符识别、自然语言处理、信息可视化等 技术实现标准文献的语义组织、知识抽取、本体构建、知识图谱、本体检索等功能。【结果】用户利用标准文献 知识服务系统, 能够获得面向知识层次的标准文献信息服务, 包括标准知识图谱和基于本体的标准知识检索服 务【结论】标准文献知识服务系统能够改善用户体验, 满足用户的标准文献知识需求。
Subjects: Library Science，Information Science >> Information Science submitted time 2017-10-11 Cooperative journals: 《数据分析与知识发现》
Abstract： [Objective] Summarize the fundamental strategies and core issues in Cross-Modal Information Retrieval (CMIR) based on correlation, and do research about the pros and cons of using partial least squares in feature subspace projection in order to improve retrieval effect. [Methods] Based on Wikipedia CMIR dataset, LDA and BOW models are used as a characteristic expression of text and image resources, cosine distance as the similarity measure, and the least squares method is used to learn subspace projection function replacing canonical correlation analysis method. [Results] Using comparative analysis of the influence of three features subspace projection methods named canonical correlation analysis, partial least squares regression, partial least squares correlation on CMIR results according to three retrieval evaluation indicators that are P@K, MAP and NDCG, and the results show that partial least squares correlation obtains the best results. [Limitations] In dealing with data, partial least squares method assumes a linear relationship between the data and an orthogonal relationship between the data base vectors, therefore the non-linear, non-orthogonal problem can not be solved. [Conclusions] Feature subspace projection learning by using partial least squares correlation is more consistent with original spatial information, and CMIR results are more stable.
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