期刊文献+

医疗信息服务应用中情境感知推荐的研究与实现 被引量:2

Study and implement of context-aware recommendation in application of medical information service
下载PDF
导出
摘要 随着智能手机用户量的不断上升,医疗信息服务逐渐应用于客户端上,同时伴随着医疗服务信息量的日益增长,移动医疗信息服务应用中出现了信息过载和用户访问效率偏低的现象。针对这种情况,对现有医疗信息服务平台在智能手机上的应用进行研究,并根据移动应用中基于地理位置服务的特殊性和LBS推荐系统的特殊要求,提出基于情境信息的医疗信息服务推荐系统框架。通过对获取到的用户位置及当前时间、天气、环境、交通等丰富的情境信息进行分析,使用基于规则的推荐方法以降低计算量,实现在线个性化智能推荐服务。结果表明,融合了丰富情境信息的移动医疗信息服务平台中的推荐,使得推荐更加个性化,更加符合用户需求。 With the increasing of the intelligent mobile phone users, the medical information service is gradually applied to the client, and at the same time, along with the increasing of medical service information, the phenomena of information over- load and low efficiency of user access appear in application of mobile medical information service. According to the particularity of location-based services and the special requirements of LBS recommendation system in mobile applications, the medical information service recommendation system framework based on the situational information was presented. Based on the analysis of the acquired user's location, and the current time, weather, environment, transportation and other rich situational information, rule-based recommended method is used to reduce the amount of calculation, and implement the online personalized intelligent recommendation service. The recommendation of mobile medical information service platform combined with rich situational information makes recommendation more personalized, and meet users' requirement more.
作者 李清明 段富
出处 《现代电子技术》 北大核心 2016年第24期58-62,共5页 Modern Electronics Technique
基金 山西省科技攻关项目(20130321001-09) 山西省科技基础条件平台计划项目(2012091003-0103) 山西省卫生厅科技攻关计划项目(2011119)
关键词 基于位置服务推荐 基于规则的推荐 情境信息 医疗信息服务平台 LBS recommendation rule based recommendation situational information medical information service platform
  • 相关文献

参考文献2

二级参考文献12

  • 1刘克骧,孙自刚,王恩康,杨铭昌,王华柳.单台测震分析辅助软件的设计及实现[J].地震地磁观测与研究,2006,27(6):112-117. 被引量:13
  • 2邓存华,李雷,杨配新,陈翔,沈道康.CDSN日常分析工作辅助软件设计[J].地震地磁观测与研究,2006,27(6):118-123. 被引量:9
  • 3Konstan J.Recommender system research:perspective and thoughts[C]//Keynote Address,Workshop on Recommender Systems:Algorithms and Evaluation,Berkeley,California,1999.
  • 4Claypool M,Gokhale A,Miranda T.Combining content-based and collaborative filters in an online newspaper[C]//Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99),Berkeley,Calif,ACM,New York.
  • 5Shahabi C.Yoda:an accurate and scalable web-based recommendation system[C]//Proceedings of the Sixth International Conference on Cooperative Information Systems,Trento,Italy,September 2001:418-432.
  • 6Mobasher B.Discovery and evaluation of aggregate usage profiles for web personalization[J].Data Mining and Knowledge Discovery,2002(6):61-82.
  • 7Burke R,Hammond K,Young B C.The FindMe approach to assisted browsing[J].Journal of IEEE Expert,1997,12(4):32-40.
  • 8Burke R.Hybrid recommender systems:survey and experiments[J].User Modeling and User-Adapted Interaction,2002,12(4):331-370.
  • 9Middleton S E,Shadbolt N R,DE Roure D C.Ontological user profiling in recommender systems[J].ACM Transactions on Information Systems,2004,22 (1):54-88.
  • 10Hyvonen E,Saarela S,Viljanen K.Application of ontology techniques to view-based semantic search and browsing[C]//Davies J.LNCS 3053:ESWS 2004,2004:92-106.

共引文献66

同被引文献30

引证文献2

二级引证文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部