| 移动网络环境下情景敏感的个性化信息推荐系统研究 |
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| 引用本文： | 周朴雄,陶梦莹.移动网络环境下情景敏感的个性化信息推荐系统研究[J].图书情报工作,2012,0(19):80-84,121. |
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| 作者姓名： | 周朴雄 陶梦莹 |
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| 作者单位： | 华南理工大学经济与贸易学院 广州 510006 |
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| 基金项目： | 国家社会科学基金项目“面向企业创新发展的行业信息服务重组研究”(项目编号:08CTQ013);广东省科技计划项目“基于数字证书的云安全认证服务支撑平台”(项目编号:2011B010200034)研究成果之一 |
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| 摘 要： | 对情景敏感和个性化需求响应是移动网络环境下信息推荐的两大触发因素。针对目前移动环境下信息服务未融入情景敏感这一因素以及信息推荐的个性化程度低和准确性差的现状,提出采用"LBS+AR+多层关联规则"三维立体式个性化信息推荐方法。采用GPS/GIS技术实现精确定位,利用基于模型的多层级关联推荐算法避免稀疏性和难于扩展问题,采用AR(增强现实)技术实现可视化的信息推送,通过构建移动网络环境下情景敏感的个性化信息推荐系统实现为用户提供个性化、可视化的信息推送服务。
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| 关 键 词： | 情景敏感 个性化推荐 多层关联规则 基于地理位置服务 增强现实 |
| 收稿时间： | 2012-03-13 |
| 修稿时间： | 2012-07-11 |
The Mobile Network Environment Situation Sensitive Personalized Information Recommendation System |
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| Zhou Puxiong Tao Mengying.The Mobile Network Environment Situation Sensitive Personalized Information Recommendation System[J].Library and Information Service,2012,0(19):80-84,121. |
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| Authors: | Zhou Puxiong Tao Mengying |
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| Institution: | Department of Economics and Trade,South China University of Technology,Guangzhou 510006 |
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| Abstract: | Sensitive to the scenario and response to the individual needs are two triggers of information recommendation in the mobile network environment. For the factor of scenario sensitive is not integrated into information service of current mobile environment, the degree of personalization is low and accuracy of the information recommended is not satisfied. On this background, a personalized recommendation method has been proposed which is a three-dimensional type of "LBS+AR+Multi-level association rules". This method adopts GPS/GIS technology to achieve accurate positioning and model-based multi-level recommendation algorithm to avoid data sparseness and scalability problems, and takes use of AR(Augmented Reality) to achieve the goal of pushing information visually. A scenario-sensitive personalized information recommendation system in the mobile network environment has also been built to provide users with personalized and visual information push service. |
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| Keywords: | situation sensitive personalized recommendation multi-level associated rules LBS AR |
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