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大数据驱动下基于情景感知的智能信息推荐研究

Research on the Big data Driven Intelligent Information Recommendation Based on Context Awareness
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摘要 大数据时代的来临引发了以"数据"驱动人类思维和决策的重大变革,利用大数据挖掘、机器学习等先进数据分析技术,从海量大数据里析取有用信息,智能服务于用户已成为信息服务的主要途径。信息推荐是一种智能信息服务机制,能根据用户兴趣自动组织和调整信息内容,是解决"信息过载"的有效工具。文章融合大数据、情景感知与信息推荐技术,研究大数据环境下用户情景的智能获取与高层推理,大数据驱动下基于情景感知的智能信息推荐流程与推荐模型。 The advent of the era of big data has triggered a major change in thinking and decision-making driven by"data".The use of advanced data analysis technologies such as big data mining and machine learning to extract useful information from massive big data has become the main way of information service.Information recommendation is an intelligent information service mechanism,which can automatically organize and adjust information content according to user interests.It is an effective tool to solve"information overload".This paper integrates big data,context awareness and information recommendation technology to study intelligent acquisition and high-level reasoning of user scenarios in big data environment,and intelligent information recommendation process and recommendation model based on context awareness driven by big data.
作者 杨君
出处 《大众科技》 2020年第10期4-6,共3页 Popular Science & Technology
基金 广东省自然科学项目“大数据环境下基于情景感知的智能信息推荐机制及其关键技术研究”(2018A030313933) 广东省哲学社会科学规划项目“大数据环境下基于情景的主动信息推荐机制研究”(GD17CTS03) 广州市哲学社会科学项目“大数据驱动的广州市信息服务业转型升级实现路径研究”(2017GZYB98)。
关键词 情景 情景感知 大数据 智能信息推荐 scene context awareness big data intelligent information recommendation
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  • 1向阳,王敏,马强.基于Jena的本体构建方法研究[J].计算机工程,2007,33(14):59-61. 被引量:33
  • 2Xiaoyuan Su,Taghi M. Khoshgoftaar,Jun Hong.A Survey of Collaborative Filtering Techniques[J]. Advances in Artificial Intelligence . 2009 被引量:4
  • 3Sonny H S C,Jiawei H,Wang K.RecTree:An Efficient Collaborative Filtering Method. Data Warehousing and Knowledge Discovery . 2001 被引量:1
  • 4Koren Y.Factorization meets the neighborhood:a multifaceted collaborative filtering model. Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2008 被引量:1
  • 5Paterek A.Improving regularized singular value decomposition for collaborative filtering. Statistics A Journal in Theoretical and Applied Statistics . 2007 被引量:1
  • 6Jian-Tao Sun,Hua-Jun Zeng,Huan Liu,et al.a novel approach to personalized Web search. Proceedings of the 14th international conference on World Wide Web . 2005 被引量:1
  • 7Robert M. Bell,Yehuda Koren.??Lessons from the Netflix prize challenge(J)ACM SIGKDD Explorations Newsletter . 2007 (2) 被引量:1
  • 8Steffen Rendle.??Factorization Machines with libFM(J)ACM Transactions on Intelligent Systems and Technology (TIST) . 2012 (3) 被引量:1
  • 9Slobodan Vucetic,Zoran Obradovic.??Collaborative Filtering Using a Regression-Based Approach(J)Knowledge and Information Systems . 2005 (1) 被引量:1
  • 10M. O’Conner,J. Herlocker.Clustering Items for Collaborative Filtering. Proceedings of the ACM SIGIR Workshop on Recommender Systems . 1999 被引量:1

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