Abstract: Aiming at the problems of the single result of recommending similar users and insufficient understanding of user interests and behavior information for single social network platforms, a User Recommendation method of Cross-Platform based on Knowledge graph and Restart random walk (URCP-KR) was proposed. First, in the similar subgraphs segmented and matched by the target platform graph and the auxiliary platform graph, an improved multi-layer Recurrent Neural Network (RNN) was used to predict the candidate user entities. And the similar users were selected by comprehensive use of the similarity of topological structure features and user portrait similarity. Then, the relationship information of similar users in the auxiliary platform graph was used to complete the target platform graph. Finally, the probabilities of the users in the target platform graph walking to each user in the community were calculated, so that the interest similarity between users was obtained to realize the user recommendation. Experimental results show that the proposed method has higher recommendation precision and diversity than Collaborative Filtering (CF) algorithm, User Recommendation algorithm based on Cross-Platform online social network (URCP) and User Recommendation algorithm based on Multi-developer Community (UR-MC) with the recommendation precision up to 95.31% and the recommendation coverage up to 88.42%.

Key words: knowledge graph, entity linking, relationship completion, restart random walk, user recommendation

摘要： 针对单一社交网络平台中推荐相似用户结果单一，对用户兴趣和行为信息了解不够全面的问题，提出了基于知识图谱和重启随机游走的跨平台用户推荐方法（URCP-KR）。首先，在分割、匹配出的目标平台图谱和辅助平台图谱的相似子图中，利用改进的多层循环神经网络（RNN）预测出候选用户实体，再综合利用拓扑结构特征相似度和用户画像相似度筛选出相似用户；然后，将辅助平台图谱中的相似用户的关系信息补全到目标平台图谱；最后，计算目标平台图谱中的用户游走到社区内每个用户的概率，从而得到用户之间的兴趣相似度来实现用户推荐。实验结果表明，与协同过滤（CF）算法、基于跨平台的在线社交网络用户推荐算法（URCP）和基于多开发者社区的用户推荐算法（UR-MC）相比，URCP-KP在推荐精确率及推荐多样性等方面均有所提高，推荐精确率最高可达95.31%，推荐覆盖率最高可达88.42%。

关键词: 知识图谱, 实体链接, 关系补全, 重启随机游走, 用户推荐