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Perception Service Framework of Digital Resources in the Pervasive Computing Environment
Received date: 2014-02-04
Online published: 2014-03-05
The ubiquitous computing concept of "people-oriented, invisible technology, focus on the task itself" brings new perspectives to the research of digital library resource discovery service. "Attention" as the service research starting point of cognition, this paper analyzes the characteristics of the digital resource service of pervasive computing environment, and constructs a "perception driver found" intelligent perception computing framework of digital resources to provide a reference for digital library knowledge service.
Key words： pervasive computing; resource discovery; intelligent perception
Qin Hong . Perception Service Framework of Digital Resources in the Pervasive Computing Environment[J]. Library and Information Service, 2014 , 58(05) : 13 -16,21 . DOI: 10.13266/j.issn.0252-3116.2014.05.002
[1] 张晓林. 从数字图书馆到E-Knowledge机制[J]. 中国图书馆学报,2005(4):5-10.
[2] 许海玲, 吴潇, 李晓东, 等. 互联网推荐系统比较研究[J]. 软件学报, 2009(2)：350-362.
[3] 刘红芝. 网络信息过滤系统的分类[J]. 图书馆学刊, 2010(4)：99-100.
[4] 牟向伟, 陈燕. 基于模糊描述逻辑的个性化推荐系统建模[J]. 计算机应用研究, 2011, 28(4)：1429-1433.
[5] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[6] Ardissono L, Kuflik T, Petrelli D. Personalization in cultural heritage: The road travelled and the one ahead[J]. User Modeling and User-Adapted Interaction, 2012, 22(1/2): 73-99.
[7] 曾子明. 移动环境下基于情境感知的数字图书馆个性化信息推荐研究[J]. 图书情报工作,2013,57(12):123-128,71.
[8] 吴金红,张飞,周磊,等.面向泛在信息环境的自适应信息服务及其关键技术分析[J].情报理论与实践,2013,36(3):112-116.
[9] 潘旭伟,李泽彪,祝锡永,等. 自适应个性化信息服务:基于情境感知和本体的方法[J]. 中国图书馆学报,2009(6):41-48.
[10] 陈定权.关联规则与图书馆书目推荐[J].情报理论与实践, 2009, 32(6)：81-84.
[11] 陈莉. 基于用户兴趣的分布式协同过滤推荐[D]. 大连：大连理工大学, 2009.
[12] 蒋翀, 费洪晓.个性化推荐系统中的混合用户偏好获取[J].计算机系统应用, 2010, 19(10)：203-206.
[13] Yu Li, Liu Lu, Li Xuefeng. A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-commerce [J]. Expert Systems with Applications, 2005, 28：67-77.
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