[1]SHI Jiarong,HE Pan.Broad collaborative filtering recommendation algorithm combined with matrix completion[J].CAAI Transactions on Intelligent Systems,2024,19(2):299-306.[doi:10.11992/tis.202209005]
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Broad collaborative filtering recommendation algorithm combined with matrix completion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 299-306 Column: 学术论文—机器学习 Public date: 2024-03-05
Title:
Broad collaborative filtering recommendation algorithm combined with matrix completion
Author(s):
SHI Jiarong;  HE Pan
School of Science, Xi’an University of Architecture & Technology, Xi’an 710055, China
Keywords:
recommendation system; broad learning system; matrix completion; broad collaborative filtering; collaborative filtering; deep matrix factorization; data sparsity; deep learning
CLC:
TP181
DOI:
10.11992/tis.202209005
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. Subsequently, 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.
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