社交-推荐网络中的隐式朋友挖掘

项目名称： 社交-推荐网络中的隐式朋友挖掘

项目编号： No.61202238

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 刘禹

作者单位： 北京航空航天大学

项目金额： 23万元

中文摘要： 伴随着社交网络与传统电子商务网络账户合并的热潮，以社交为手段、以商品推荐为目的的社交-推荐网络已经形成。本研究充分结合用户社交行为和商品行为历史,对目标用户的社交好友隐式推荐关系进行分层次挖掘：对一跳朋友，用同现、时序和共同朋友关系统计社交活动量，用拓扑势模型度量朋友圈内威望，另结合用户间商品行为历史相似度，综合三个因素挖掘其中的推荐主导群体；对二跳朋友，建模用户的偶遇关系，以用户间的商品行为相似性为指导，通过机器学习方法推导偶遇行为次数与推荐影响力的关联，预测隐藏的具有较强推荐影响的间接朋友；对外围用户，通过商品相似性与商品行为历史相似性比较，挖掘共同兴趣朋友，作为社交网络冷启动情况下推荐依据的补充。研究目标是挖掘社交-推荐网络中对目标用户最具影响力的用户群体，其结果对揭示社交-推荐网络中用户推荐影响力的构成、度量与传播规律有重要意义，可为新兴的社交-推荐网络的商品推荐系统构建提供思路。

中文关键词： 社交网络；标签同现；社交关系强度；推荐系统；

英文摘要： With the accounts incorporation boom of social networks and the traditional e-commerce networks, the social recommendation network with purpose of commodity recommendation has been emerging by the ways of social interaction. In this research, the implicit recommendation relationships among target user's social friends were mined hierarchically by fully integrating user's social behavior and commodity's buying history. For friends within one hop, we calculate the social activity number by the co-occurrence, timing and mutual friend relationships.And mesure the influence in the circle of friends by topological potential model.The similarities of buying histories among users were also calculated.Then we can abtain the dominant group for recommendation by incorporate the above three factors.For friends within two hops, we modelled the chance meeting relationship. With the guide of buying histories similarity among uses, the indirect friends who were much more influential were predicted by machine learning methods to infer the associations between the number of chance meeting and recommendation influence. For the external friends, we find the friends with common interests by comparing similarities between commodity and user's buying histories. The cold-start problem was resolved by this case. The research goal is to

英文关键词： social network；tag co-occurrence；socialtie strength；recommendation system；