基于全加权矩阵分解的用户协同过滤推荐算法

摘要/Abstract

摘要： 针对传统的基于用户协同过滤推荐算法将用户对某物品的喜好程度等同看待的问题,文中提出了一种融合全加权矩阵分解的用户协同过滤模型。该模型首先为观测值设计频率感知加权,且非均匀地设计用于未观测值的用户导向加权。然后组合观测值和未观测值的加权,并根据评分确定用户声誉和用户关系的相似性,构建融合全加权矩阵分解的用户协同过滤模型。为了验证提出的推荐算法的性能,在Douban、Epinions和Last.fm 3个真实数据集上进行了仿真实验。实验结果表明,所提出的AWMF_UCFR算法的推荐准确性与MF算法、WRMF-UO算法、SoRS算法相比有显著提高。

关键词: 全加权矩阵分解, 社交网络, 推荐算法, 协同过滤

Abstract: Aiming at the problem that traditional user collaborative filtering recommendation algorithm equates users’ preferences for an item,a user collaborative filtering model based on all weighted matrix decomposition was proposed.Firstly,the model designs frequency sensing weights for observations,and non-uniformly designs user-oriented weights for unobserved values.Then,the weights of the observed and unobserved values are combined,and the similarity between user reputation and user relationship is determined according to the score,and the user collaborative filtering model of the fused fully weighted matrix decomposition is constructed.In order to verify the performance of the proposed recommendation algorithm,experiments were carried out on three real data sets:Douban,Epinions and Last.fm.The experimental results demonstrate that the proposed AWMF_UCFR algorithm achieves significant improvements on recommendation accuracy than MF algorithm,WRMF-UO algorithm and SoRS algorithm.

Key words: All-weighted matrix factorization, Collaborative filtering algorithm, Recommendation algorithm, Social network

中图分类号:

TP391

邓秀勤, 刘太亨, 刘富春, 龙咏红. 基于全加权矩阵分解的用户协同过滤推荐算法[J]. 计算机科学, 2019, 46(11A): 199-203. https://doi.org/

DENG Xiu-qin, LIU Tai-heng, LIU Fu-chun, LONG Yong-hong. User Collaborative Filtering Recommendation Algorithm Based on All Weighted Matrix Factorization[J]. Computer Science, 2019, 46(11A): 199-203. https://doi.org/

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