摘要
由于推荐系统中数据稀疏性和冷启动问题日益严重,传统的算法无法有效地解决这些问题,现有的改进算法由于稳定性差,仍然需要预先确定具体的参数。文章提出了一种基于社区结构的冷启动算法以改善推荐系统中的冷启动问题,通过计算用户和项目节点的相似度投影二分网络,利用改进的Louvain算法对投影单模式网络进行社区检测,使新记录更新到社区,然后进行对用户社区组推荐。与其他冷启动算法相比,该算法在推荐准确率和运行效率有明显提升。
Due to the increasingly serious problems of data sparsity and cold start in recommendation systems,traditional algorithms cannot effectively solve these problems,and the existing improved algorithms still need to pre-determine specific parameters due to their poor stability.In this paper,a cold-start algorithm based on community structure is proposed to improve the cold-start problem in recommendation systems.By calculating the similarity bipartite projection network between users and project nodes,the improved Louvain algorithm is used to detect the community for the projected single-mode network.It updates the new record to the community and then makes recommendations to the user community groups.Compared with other cold-start algorithms,the algorithm has a significant improvement in recommendation accuracy and operating efficiency.
作者
曹珂崯
张天舒
孙娟
彭二帅
CAO Keyin;ZHANG Tianshu;SUN Juan;PENG Ershuai(Department of Computer and Communication,Jiangsu Vocational College of Electronics and Information,Huaian 223001,China)
出处
《现代信息科技》
2023年第1期30-32,35,共4页
Modern Information Technology
关键词
社区检测
冷启动
二分投影网络
推荐系统
community detection
cold start
bipartite projection network
recommendation system
