期刊文献+

基于情境感知的移动电子资源推荐技术研究 被引量:12

Research on the Mobile Electronic Resources Recommendation Technology Based on Context Awareness
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摘要 将情境因素引入个性化推荐系统中,考虑用户—资源—情境之间的三元关系,为处于不同情境、不同兴趣的移动用户推荐满足其需求的合适信息服务,是目前信息推荐新的研究方向。文章提出了基于情境和基于内容相结合的推荐算法,在当前情境下,利用用户的历史情境信息和用户偏好综合为用户推荐信息。实验表明,该算法能够显著地提高个性化推荐的准确率,可为用户提供符合当前情境的个性化资源。 To introduce context awareness into personalized recommendation system and consider the ternary relation of user- resources-context, which can recommend appropriate information services to meet the needs of mobile users with different interests in different contexts, is the new research tend of information recommendation. This paper proposes a recommendation algorithm on the basis of the combination of context-based and content-based. In current context, the paper uses historical context information and users preference to recommend information for users. The experiment shows that the algorithm can significantly improve the accuracy of personalized recommendation, which can provide appropriate personalized resources to users in current context.
作者 田雪筠
出处 《情报理论与实践》 CSSCI 北大核心 2015年第5期86-89,104,共5页 Information Studies:Theory & Application
关键词 情境感知 移动用户 个性化推荐 context awareness mobile user personalized recommendation
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  • 1庄贵军,周南,李福安.情境因素对于顾客购买决策的影响(一个初步的研究)[J].数理统计与管理,2004,23(4):7-13. 被引量:21
  • 2祝锡永,潘旭伟,王正成.基于情境的知识共享与重用方法研究[J].情报学报,2007,26(2):179-184. 被引量:22
  • 3张光卫,李德毅,李鹏,康建初,陈桂生.基于云模型的协同过滤推荐算法[J].软件学报,2007,18(10):2403-2411. 被引量:193
  • 4中国互联网络信息中,心.第36次中国互联网络发展状况统计报告[EB/OL].http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/201507/P020150723549500667087.pdf,2015-09-01.
  • 5GHAZANFAR H. Comparison of metrics for feature selection in imbalaneed text classification [ J ]. Expert Systems with Appli- cations, 2011, 38 (5): 4978-4989.
  • 6SARWAR B, KARYPIS G, KONSTAN J. Application of di- mensionality reduction in recommender system--a ease study [R]. ACM Web KDD 2000 Workshop, 2000: 1-15.
  • 7SHILAD I. Estimating attributes: analysis and extensions of re- lief [J]. Machine Learning, 2011, 8 (4) : 171-182.
  • 8ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions [ J ]. IEEE Trans on Knowledge and Data Engineering, 2005, 17 (6): 734-749.
  • 9SRINIVASA N, MEDASANI S. Active fuzzy clustering for col-laborative filtering [ C ]. 2004 IEEE International Conference on Fuzzy Systems, Budapest, Hungary, 2004: 1697-1702.
  • 10SUNGHWAN M, INGOO H. Dynamic fuzzy clustering for rec- ommender systems [ C ]. Pacificasia Conference on Advances in Knowledge Discovery and Data Mining, 2005: 480-485.

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