A Latent Factor Model Based on Elastic Network for Recommender Systems
Authors: 
Dexian Wang, Ping Deng, Chunlin He, Baolin Li, Rui LiuAuthors Info & Claims
Pages 78 - 86
Abstract
In recommendation systems, Latent factor (LF) models are highly efficient for analyzing the LF problem that is defined on high-dimensional and sparse (HiDS) matrices corresponding to relationships among numerous entities in industrial applications. Current models mostly adopt l2 norm-based regularization, which cannot regularize the latent factor distributions. Therefore, regularization schemes are important to improve the properties of an LF model. This work integrates the elastic network optimization scheme into an LF model, where utilized on HiDS matrices. We further adopt an efficient learning algorithms, i.e., stochastic proximal gradient descent, to train desired latent factors in an SENLF-based model, resulting in a novel SENLF-based models relying on the learning scheme. Experimental results on two HiDS matrices arising from industrial applications indicate that an elastic-network latent factor is able to ensure the high prediction accuracy, high computational efficiency, and describe sparse LFs distribution.
References
[1]
Koren, Y. and Bell. R. 2011. Advances in Collaborative Filtering: Springer US.
[2]
Luo, X., Zhou, M. C., Xia, Y., et al. 2015. Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models. IEEE Transactions on Neural Networks & Learning Systems, 27(3):524--537.
[3]
Tikk, D. 2009. Scalable Collaborative Filtering Approaches for Large Recommender Systems. Journal of Machine Learning Research, 10(10):623--656.
[4]
Shi, Y., Larson, M., Hanjalic, A. Collaborative Filtering beyond the User-Item Matrix{J}. Acm Computing Surveys, 2014, 47(1):1--45.
[5]
Adomavicius, G. and Tuzhilin, A. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge & Data Engineering, 17(6), 734--749.
[6]
Cai, Y., Leung, H., Li, Q., Min, H., Tang, J., and Li. J. Mar. 2014. Typicality-based collaborative filtering recommendation. IEEE Trans. Knowledge and Data Engineering, 26, 766--779.
[7]
Yao, L., Sheng, Q. Z., Ngu, A. H. H., Yu, J., and Segev, A. 2015. Unified Collaborative and Content-Based Web Service Recommendation. IEEE Transactions on Services Computing, 8(3), 453--466.
[8]
Hofmann, T. 2004. Latent semantic models for collaborative filtering: ACM.
[9]
Huang, K., Sidiropoulos, N. D., and Swami, A. 2013. Non-Negative Matrix Factorization Revisited: Uniqueness and Algorithm for Symmetric Decomposition. IEEE Transactions on Signal Processing, 62(1). 211--224.
[10]
Luo, X., Shang, M., and Li, S. Efficient Extraction of Non-negative Latent Factors from High-Dimensional and Sparse Matrices in Industrial Applications. IEEE International Conference on Data Mining, 311--319.
[11]
Yu, D., Liu, Y., Xu, Y., and Yin, Y. Personalized QoS Prediction for Web Services Using Latent Factor Models. the 11th IEEE Int. Conf. on Services Computing, 107--114.
[12]
Ma, H., Zhou, D., Liu, C., Lyu, M. R., and King, I. Recommender systems with social regularization. the 4th ACM International Conference on Web Search and Data Mining, 287--296.
[13]
Jin, B., Che, C., Yu, K., Qu, Y., Guo, L., Yao, C., Yu, R., and Zhang, Q. Minimizing Legal Exposure of High-Tech Companies through Collaborative Filtering Methods. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 127--136.
[14]
Luo, X., Zhou, M., Xia, Y., and Zhu, Q. 2014. An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems. IEEE Transactions on Industrial Informatics, 10(2), 1273--1284.
[15]
Mairal, J., Bach, F., Ponce, J., and Sapiro, G. 2009. Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research, 11(1), 19--60.
[16]
Tibshirani, R. 2011. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society, 73(3), 273--282.
[17]
Zou, H. and Hastie, T. 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, 67(2), 768--768.
[18]
Beck, A. and Teboulle, M. 2009. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems: Society for Industrial and Applied Mathematics.
[19]
Nesterov, Y. 2004. Introductory Lectures on Convex Optimiza-tion. Applied Optimization, 87(5), 236.
[20]
Nitanda, A. Stochastic proximal gradient descent with accel-eration techniques. International Conference on Neural In-formation Processing Systems, 1574--1582.
[21]
Luo, X., Zhou, M.-C., Li, S., You, Z.-H., Xia, Y.-N., and Zhu, Q.-S. 2016. A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Di-rection Method," IEEE Trans. on Neural Networks and Learning Systems, 27, 579--592.
[22]
Koren, Y., Bell, R., and Volinsky, C. 2009. Matrix factorization techniques for recommender systems. IEEE, Computer Journal, 42(8), 30--37.
[23]
Lu, Z., Bai, H., and Sun, B. A gradient-based algorithm for optimizing sensing matrix with normalization constraint. IEEE 11th Conference on Industrial Electronics and Applica-tions, 2376--2380.
[24]
Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. 2004. Least Angle Regression. Annals of Statistics, 32(2), 407--451.
[25]
Langford, J., Li, L., and Zhang, T. 2008. Sparse Online Learning via Truncated Gradient. Journal of Machine Learning Research, 10(2), 777--801.
Index Terms
- A Latent Factor Model Based on Elastic Network for Recommender Systems
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Published In
August 2018
106 pages
ISBN:9781450365826
DOI:10.1145/3297730
Copyright © 2018 ACM.
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In-Cooperation
- Southwest Jiaotong University
- Harbin Inst. Technol.: Harbin Institute of Technology
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 August 2018
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- Research-article
- Research
- Refereed limited
Funding Sources
- Sichuan Provincial Department of Education
- Nanchong Science and Technology Support Project
- China West Normal University Talent Research Fund Project
- National College Students Innovation and Entrepreneurship Training Program
Conference
BDET 2018
BDET 2018: 2018 International Conference on Big Data Engineering and Technology
August 25 - 27, 2018
Chengdu, China
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