欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
社交网络服务需要响应用户实时、连续、个性化的服务需求。然而，目前多数社交网络服务并未充分考虑用户的个性化服务需求。由于社交网络中海量的数据更新使得提供实时个性化服务成为一项相对艰巨的任务。文中基于LDA主题模型推断微博的主题分布和用户的兴趣取向，提出了微博系统上用户感兴趣微博的实时推荐方法，以响应用户实时、连续和个性化的服务请求，在真实数据集上的实验结果验证了文中提出的方法的有效性和高效性。
英文摘要：
Social network services need to response to the real-time,continuous and personalizedrequirements.However,most social network services do not personalize user requirements.Large volume updates of user generated content result in a challenging task that a social networksystem provides a real-time,continuous and personalized service.To better response to the real-time,continuous and personalized requirements,in this paper,we infer the topic distribution ofmicroblogs and interest vector of a user based on the LDA model.Based on the topic model,wefurther recommend user interested microblogs which are published recently on microbloggingsystems.Finally,we have conducted extensive experiments on the real dataset,and elaboratedthe effectiveness and efficiency of our proposed method.
同期刊论文项目
同项目期刊论文
期刊信息
位置：