社会网络环境下基于用户-资源关联的信息推荐研究

项目名称： 社会网络环境下基于用户-资源关联的信息推荐研究

项目编号： No.71303178

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 管理科学

项目作者： 胡吉明

作者单位： 武汉大学

项目金额： 20万元

中文摘要： 社会网络环境下，用户信息需求和行为模式及资源组织方式发生了变革，要求从用户与资源之间的关联结构入手，面向用户偏好社区和基于资源内容语义挖掘进行社会化信息推荐原理、策略及方法的创新实现研究。本项目立足于包含用户偏好建模、资源内容挖掘和两者匹配的推荐实现框架，从用户社会化需求及社群行为分析出发，研究用户与资源交互形成的关联结构；基于改进的关系社区发现算法进行用户需求偏好的准确描述和建模，结合基于潜在语义分析的主题挖掘和语义向量空间模型进行语义层次上的资源内容特征建模；在此基础上得到用户与资源之间的概率偏好关系，进而构建用户-资源关联图，引入物理动力学中的物质扩散和热传导能量分配原理，构建基于用户-资源二部关联图的社会化推荐模型，结合典型数据集的实验和网络服务案例分析，实现面向用户小众社群需求的个性化和多样化推荐。本项目将拓展当前信息资源组织和推荐理论，对提升推荐效果和服务质量具有实际意义。

中文关键词： 社会网络环境；用户-资源关联；信息推荐；能量分配；语义建模

英文摘要： Under social networking environment, the pattern of users' information needs and behavior, and the way of resource organization have been reforming, which demands change in the innovative implementation of information recommendation. That is, considering the associative architecture or structure formed by the interaction between users and information resources, user's preference community oriented and based on resources' semantic mining, this project aims to explore and study the principles, strategies, methods and their implementations of socialization recommendation. According to the general study framework of recommendation implementation, mainly including users' preference modeling, resources content mining and their matching, this project analyzes users' socialization needs and community behavior under social networking environment, and studies the characteristics of associative structure formed by the interaction between users and resources. On the basis of that, the accurate describing and modeling of users' needs and preference based on the improved discovery algorithm of relationship community are achieved, and the modeling of resources content on semantic level combining the topic mining of content based on latent semantic analysis and semantic space vector model is studied. Furthermore, the implementa

英文关键词： Social networking environment；Association of users-resources；Information recommendation；Energy distribution；Semantic modeling