大数据环境下融合多源信息的推荐系统关键问题研究

项目名称： 大数据环境下融合多源信息的推荐系统关键问题研究

项目编号： No.71471016

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 管理科学

项目作者： 甘明鑫

作者单位： 北京科技大学

项目金额： 60万元

中文摘要： 本项目针对大数据环境下推荐系统面临的信息多样化和异质性挑战，提出大数据环境下融合多源信息的推荐系统关键问题研究。项目研究推荐系统多源信息的网络构建、多源信息的网络融合以及基于信息融合的推荐等关键问题，并提出有效的解决途径。项目首先从网络分析的视角将多种信息转化为多个网络。然后从网络融合和信息融合两条思路进行研究。基于网络融合的思想，提出融合多个网络的方法，以及基于网络融合进行推荐的统计学和图论方法。基于信息融合的思想，建立直接基于多源信息进行推荐的统计学和机器学习方法。最后将所提出方法应用于特定应用领域，筛选效果较好的解决方案。项目从系统的、整合的、量化的观点研究大数据环境下融合多源信息的推荐系统关键问题，旨在充分利用多种信息提高推荐的精确性、新颖性和多样性。项目推广应用具有坚实理论基础的统计学、机器学习和图论方法，为运用这些方法解决信息管理领域的重要科学问题提供有益借鉴。

中文关键词： 智能决策支持系统；商务智能；数据挖掘；电子商务；信息系统

英文摘要： In the big data environment, the variety and heterogeneity of information has becoming a great challenge for recommender systems. Rising to this challenge, we propose in this project to study critical issues of multi-source information fusion for recommender systems. We study such critical issues as network construction, network integration for multi-source information and information-fusion based recommendation, and then propose effective solutions to solve them. From the viewpoint of network analysis, we propose to convert multiple information sources into multiple networks. Then, we propose two strategies as network fusion and information fusion to address such critical issuses. First, we suggest to integrate the multiple networks and then develop new recommendation methods from the perspective of statistics and graph theory. Second, we propose to directly fuse these information sources and establish statistics and machine learning methods for recommendation. Finally, we use the proposed methods to certain application fields and select several solutions with better performances. We study the crucial issues of how to fuse multi-source information for recommendation based on such opinions as systems, integrity and quantification. The objective of this research is to more sufficiently utilize multiple information sources, for the purpose of improving recommendation accuracy, novelty and diversity. We places great importance on statistical methods and graph theory with a solid foundation, and thus will provide helpful experiences in future applications of these theories and methods to crucial scientific issues in information management field.

英文关键词： Intelligent decision support systems;Business intelligence;Data mining;Electronic commerce;Information systems