作　　者： ;

机构地区： 广东工业大学华立学院

出　　处： 《信息技术》 2020年第2期130-134,共5页

摘　　要： 为了提高社交网络的用户信息发现和推荐能力,提出一种基于模糊时空信息感知的社交网络跨层融合性智能推荐算法。根据用户的社会关系构建社交网络用户节点的时间和位置联合分布模型,采用交叉性自适应学习方法进行社交网络用户偏好信息挖掘和关联特征检测,结合用户属性提取社交网络用户的跨层属性特征集,采用模糊感知和深度学习技术实现社交网络用户信息的深度融合处理,采用用户的协同过滤算法进行用户自动匹配,根据身份匹配结果实现社交网络融合性智能推荐。仿真结果表明,采用该方法进行社交网络信息推荐的准确性较高,推荐信息与用于的偏好匹配性较好。 In order to improve the ability of user information discovery and recommendation of social networks,an intelligent recommendation algorithm for cross-layer fusion of social networks based on fuzzy spatiotemporal information perception is proposed.According to the social relations of users,the time and location joint distribution model of social network user nodes is constructed,and the cross-adaptive learning method is used to mine user preference information and detect association features.Combined with user name attribute to extract the cross-layer attribute feature set of social network users,fuzzy perception and depth learning technology is used to realize the deep fusion of social network user information,and the user's collaborative filtering algorithm is used to automatically match users.According to the result of identity matching,the intelligent recommendation of social network fusion is realized.The simulation results show that the proposed method is more accurate and the preference matching between the recommended information and the used information is better.

关 键 词： 模糊感知 社交网络 信息融合 推荐算法

领　　域： [自动化与计算机技术—计算机应用技术] [自动化与计算机技术—计算机科学与技术]