摘要
为有效地捕获数据的潜在结构并且降低计算量,提出一种基于无向加权图上信号采样重构的推荐系统预测算法。为了利用未标记条目所携带的信息,将用户或项目及其关系建模为一个加权无向图。为了实现采样信号重构,在再生核希尔伯特空间中,将该问题近似地建模为一个二次无条件优化问题。为了降低计算复杂度,引入一种近似求解策略。在两个开放的公共数据库上的实验结果表明,该模型显著提高了预测精度,并且大大降低了计算复杂度。
In order to effectively capture the potential structure of data and reduce the amount of computation,a recommender system prediction algorithm based on signal sampling reconstruction on undirected weighted graph is proposed.In order to utilize the information carried by unmarked items,users or items and their relationships were modeled as a weighted undirected graph.In order to reconstruct the sampled signal,the problem was approximately modeled as a quadratic unconditional optimization problem in reproducing kernel Hilbert space.In order to reduce the computational complexity,an approximate solution strategy was introduced.The experimental results on two open public databases show that the model significantly improves the prediction accuracy and greatly reduces the computational complexity.
作者
刘爱民
李茂
Liu Aimin;Li Mao(Chenzhou Vocational and Technical College,Chenzhou 423000,Hunan,China;School of Information Science and Engineering,Hunan University,Changsha 410000,Hunan,China)
出处
《计算机应用与软件》
北大核心
2024年第8期326-333,397,共9页
Computer Applications and Software
基金
湖南省自然科学基金项目(2015JJ2027)。
关键词
推荐系统
采样重构
希尔伯特空间
加权无向图
Recommender system
Sampling reconstruction
Hilbert space
Weighted undirected graph
