基于知识图谱的多粒度社交网络用户画像构建方法

Multi-grained social network user portrait construction method based on knowledge graph

针对网络中社交用户属性信息划分不明确的问题,提出了一种基于知识图谱的多粒度社交用户画像构建方法。利用知识图谱映射用户信息,结合图谱分类划分映射数据多维度的属性类别,建立了社交用户集。赋予集合中每位用户对应属性标签的同等权重值,计算权重参数及兴趣标签频次,再根据用户点击频次信息,构建多粒度社交网络中用户的连续活动位置序列及活动范围序列,统一计算和划分序列中每位用户的特征,完成用户画像建立。仿真实验证明,本文方法用户画像构建时间不超过25 ms,对应属性匹配重叠数量最高为705,匹配度较高,说明所构建的用户画像信息划分较明确。

Aiming at the ambiguity of attribute information partition of social users in network,a multi-granularity social user image construction method based on knowledge graph was proposed.Using knowledge graph to map user information and classify multi-dimensional attributes of mapped data,a set of social users was established.The same weight value of each user's corresponding attribute tag in the set was given,and the weight parameters and the frequency of interest tag were calculated.Then,according to the click-frequency information of users,a sequence of continuous active locations and active ranges of users in a multi-granularity social network was constructed,and the features of each user in the sequence were calculated and divided in a unified manner to complete the establishment of user images.Simulation experiments show that the user profile construction time under the proposed method does not exceed 25 ms,and the maximum number of matching overlaps of corresponding attributes is 705.The matching degree is high,indicating that the constructed user profile information is clearly divided.