The application of traditional collaborative filtering algorithm on large-scale commercial websites is very mature. However, the data sparsity and extensibility problems that occur in the algorithm affect the recommendation accuracy of the algorithm. In order to solve this problem, a SOM clustering collaborative filtering algorithm based on singular value decomposition is proposed. Firstly, the original sparse matrix is reduced by the singular value decomposition, and the items are evaluated in the low-dimensional space, the prediction results are filled in the original matrix, which alleviates the problem of data sparseness. Then use SOM to cluster the users, which reduces the range of users searching for neighbors and improves the scalability of the algorithm. The experimental results on MovieLens-100k show that the algorithm can effectively improve the accuracy of the recommendation.

References

[1]

Gantz J, Reinsel D. The Digital Universe in 2020:Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. IDC iView:IDC Analyze the Future, 2012. 1--16.

Google Scholar

[2]

Kermany N R, Alizadeh S H. A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques{J}. Electronic Commerce Research & Applications, 2017, 21:50--64.

Digital Library

Google Scholar

[3]

Su, Xiaoyuan, Khoshgoftaar, et al. A survey of collaborative filtering techniques{J}. Advances in Artificial Intelligence, 2009, 2009(12):4.

Digital Library

Google Scholar

[4]

Cacheda F, Formoso V. Comparison of collaborative filtering algorithms:Limitations of current techniques and proposals for scalable, high-performance recommender systems{J}. Acm Transactions on the Web, 2011, 5(1):1--33.

Digital Library

Google Scholar

[5]

Chen L C, Kuo P J, Liao I E. Ontology-based library recommender system using MapReduce{M}. 2015.

Digital Library

Google Scholar

[6]

Sarwar B M, Karypis G, Konstan J A, et al. Application of Dimensionality Reduction in Recommender System -- A Case Study{C}// ACM Webkdd Workshop. 2000.

Crossref

Google Scholar

[7]

Massa P, Avesani P. Trust-Aware Collaborative Filtering for Recommender Systems{M}// On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE. Springer Berlin Heidelberg, 2004:492--508.

Google Scholar

[8]

Wang C, Blei D M. Collaborative topic modeling for recommending scientific articles{C}// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2011:448--456.

Digital Library

Google Scholar

[9]

Salakhutdinov R, Mnih A. Probabilistic Matrix Factorization.{J}. Advances in Neural Information Processing Systems, 2007:1257--1264.

Digital Library

Google Scholar

[10]

Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model{C}// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2008:426--434.

Google Scholar

[11]

Arkadiusz Paterek. Improving regularized singular value decomposition for collaborative filtering{J}. Proceedings of Kdd Cup & Workshop, 2007.

Google Scholar

[12]

Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo{C}// International Conference on Machine Learning. ACM, 2008:880--887.

Digital Library

Google Scholar

[13]

Wang Q M, Liu X, Zhu R, et al. A New Personalized Recommendation Algorithm of Combining Content-based and Collaborative Filters{J}. Computer & Modernization, 2013, 1(8):64--67.

Google Scholar

[14]

Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. IEEE, Computer Journal, 42(8), 30--37{J}. 2009, 42(8):30--37.

Digital Library

Google Scholar

Cited By

View all

Noulapeu Ngaffo AChoukair Z(2022)A deep neural network-based collaborative filtering using a matrix factorization with a twofold regularizationNeural Computing and Applications10.1007/s00521-021-06831-934:9(6991-7003)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00521-021-06831-9
Ngaffo AAyeb WChoukair Z(2020)A Bayesian Inference Based Hybrid Recommender SystemIEEE Access10.1109/ACCESS.2020.29988248(101682-101701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2998824

Index Terms

SOM Clustering Collaborative Filtering Algorithm Based on Singular Value Decomposition
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Non-negative matrix factorization

Recommendations

Combining Singular Value Decomposition and Item-based Recommender in Collaborative Filtering
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining

Recommender Systems are introduced as an intelligent technique to deal with the problem of information and product overload. Their purpose is to provide efficient personalized solutions in economic business domains. Collaborative filtering is a widely ...
Collaborative filtering recommendation algorithm integrating time windows and rating predictions

This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization ...
A Rough Set-Based Clustering Collaborative Filtering Algorithm in E-commerce Recommendation System
ICIII '09: Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 04

Rough set is a new mathematical tool that deal with incomplete and uncertain knowledge, it can improve the classification accuracy because of its characteristics. Recommendation algorithm is the core of the recommendation system. In this paper, a rough ...

Comments

Information & Contributors

Information

Published In

ICMAI '19: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence

April 2019

232 pages

ISBN:9781450362580

DOI:10.1145/3325730

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Southwest Jiaotong University
Xihua University: Xihua University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 April 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICMAI 2019

ICMAI 2019: 2019 4th International Conference on Mathematics and Artificial Intelligence

April 12 - 15, 2019

Chegndu, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
85
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Noulapeu Ngaffo AChoukair Z(2022)A deep neural network-based collaborative filtering using a matrix factorization with a twofold regularizationNeural Computing and Applications10.1007/s00521-021-06831-934:9(6991-7003)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00521-021-06831-9
Ngaffo AAyeb WChoukair Z(2020)A Bayesian Inference Based Hybrid Recommender SystemIEEE Access10.1109/ACCESS.2020.29988248(101682-101701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2998824

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Combining Singular Value Decomposition and Item-based Recommender in Collaborative Filtering

Collaborative filtering recommendation algorithm integrating time windows and rating predictions

A Rough Set-Based Clustering Collaborative Filtering Algorithm in E-commerce Recommendation System

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations