面向社会网络用户需求的推荐系统研究

项目名称： 面向社会网络用户需求的推荐系统研究

项目编号： No.61300070

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 张日崇

作者单位： 北京航空航天大学

项目金额： 25万元

中文摘要： 随着网络技术的日益成熟，人们逐渐依赖互联网获取信息，交流沟通。社会网络技术的成长使得社会网络中用户间的交流越来越密切，社会网络逐渐成为用户进行信息获取和交互的重要平台。社会化媒体在方便用户交流的同时，也导致用户面对大量信息而无法判断其可靠性。传统推荐系统在处理大规模社会网络数据时往往会遇到计算效率和算法性能等瓶颈，社会网络中数据的多样化也需要提供新的用户建模方法和信息推荐模型满足社会网络用户需求。本课题面向社会网络用户对社会产品评分，社会媒体内容和社会成员关系三个方面的推荐需求展开研究,拟提出分布式自适应的协同过滤计算方法克服传统推荐系统处理大规模数据的性能瓶颈；采用图模型对社会网络中的传播内容与用户间关系进行建模，分析用户的潜在偏好并基于生成者和接受者的偏好分布生成媒体内容推荐；通过对社会网络中成员信任关系使用数学方法建模,建立用户信任关系传递模型并给出可信社会成员推荐。

中文关键词： 推荐系统；社交网络；链接预测；用户建模；

英文摘要： At present, users and contents over the social network are growing dramatically. Internet users retrieve, communicate, and share information in the social network. The development of social media helps the spreading of informaiton. At the same time, however, users have to analyze the credibility of the massive communicated information, mostly generated by users, before making information consumption. Based on users' various information requirements, we proposed novel approachs for developing distributed collaborative filtering algorithms; building social media user preferences to characterize the user behavior over the social media; designing trust model to discover the trust relationships between social network users. More specifically, we intent to pursue researches on the following aspects: Distributed Adaptive Collaborative Filtering (CF): To overcome the limitation of performance of the traditional CF for dealing with massive user rating data, we proposed a divide-and-conqure approach for the item-based recommender system. In this study, we advocate that the ranking performance is more important than the average predicted ratings for recommender system. We also provide an incremental learning, such that parameters can be easily updated as new ratings becomes available. Content Recommendation based on the U

英文关键词： Recommender System；Social Network；Link Prediction；User Profiling；