摘要
由于真实社交网络存在非连通性以及大量孤立节点,无法精准刻画节点间社交关系强度,社会化推荐模型的数据稀疏和冷启动等问题得不到有效缓解。针对该情况,提出一种全局视域下基于重启随机游走算法的社会化推荐模型。引入超级节点构造有向连通网络;运用重启随机游走算法刻画节点间社交关系强度;将刻画后的社交关系强度融入到基于概率分解技术的社会化推荐模型。实验结果表明,与传统社会化推荐模型对比,该模型能有效提升推荐效果。
Due to the non-connectivity and a large number of isolated nodes in real social networks,it is impossible to accurately describe the strength of social relationships between nodes,so that the problems of data sparseness and cold start of social recommendation models cannot be alleviated.In view of this situation,a social recommendation model based on the restart random walk algorithm is constructed in the global view.The model introduced super nodes to construct a directed connected network,used the restart random walk algorithm to characterize the strength of social relations between nodes,and integrated the characterised social relations strength into a social recommendation model based on probability decomposition technology.The experimental results show that compared with the traditional recommendation model,this model can effectively improve the recommendation effect.The experimental results show that compared with traditional social recommendation models,this model can effectively improve recommendation performance.
作者
李旭军
殷孜
胡启兵
曹国
Li Xujun;Yin Zi;Hu Qibing;Cao Guo(School of Economics and Management,Changzhou Institute of Technology,Changzhou 213032,Jiangsu,China)
出处
《计算机应用与软件》
北大核心
2025年第9期331-340,共10页
Computer Applications and Software
基金
国家社会科学基金项目(21BGL036)。
关键词
社会化推荐
超级节点
有限状态马氏链
冷启动
Social recommendation
Super node
Finite state Markov chain
Cold start
作者简介
李旭军,副教授,主研领域:智能推荐系统,算法管理;殷孜,讲师;胡启兵,副教授;曹国,副教授。
