智能系统学报2024，Vol.19Issue(2)：299-306,8.DOI:10.11992/tis.202209005
结合矩阵补全的宽度协同过滤推荐算法
Broad collaborative filtering recommendation algorithm combined with matrix completion
摘要
Abstract
Collaborative filtering is a classic method used in recommendation systems,designed to cater to the need for personalized recommendations.However,many collaborative filtering algorithms struggle when confronted with sparse rating data.To address this issue,we propose a broad collaborative filtering recommendation algorithm that integrates matrix completion.Initially,a matrix completion technique is employed to recover the user-item rating matrix.Sub-sequently,this completed rating matrix is utilized to identify respective neighbors for a given user-item pair,which in turn helps create the user-item rating collaboration vector.Finally,a broad learning system is employed to establish the complex nonlinear relationship between user-items and ratings.The effectiveness of the proposed algorithm has been validated through tests on MovieLens and Filmtrust data sets.The experimental results show that,compared with state-of-the-art collaborative filtering methods,the proposed method can effectively alleviate the data sparsity problem.It also possesses lower computational complexity and enhances recommendation performance to a certain extent.关键词
推荐系统/宽度学习系统/矩阵补全/宽度协同过滤/协同过滤/深度矩阵分解/数据稀疏性/深度学习Key words
recommendation system/broad learning system/matrix completion/broad collaborative filtering/collaborat-ive filtering/deep matrix factorization/data sparsity/deep learning分类
计算机与自动化引用本文复制引用
史加荣,何攀..结合矩阵补全的宽度协同过滤推荐算法[J].智能系统学报,2024,19(2):299-306,8.基金项目
陕西省自然科学基金项目(2021JM-378). （2021JM-378）
