A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

By Xin Luo, Mengchu Zhou, Shuai Li, Zhu-Hong You, Yunni Xia, Qingsheng Zhu. IEEE Transactions on Neural Networks and Learning Systems, 2016

315 citations recorded by the source corpus.

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and storage complexity, as well as slow convergence rate, which prevents them from industrial usage in context of big data. To address these issues, this paper proposes an alternating direction method (ADM)-based nonnegative latent factor (ANLF) model. The main idea is to implement the ADM-based optimization with regard to each single feature, to obtain high convergence rate as well as low complexity. Both computational and storage costs of ANLF are linear with the size of given data in the target matrix, which ensures high efficiency when dealing with extremely sparse matrices usually seen in CF problems. As demonstrated by the experiments on large, real data sets, ANLF also ensures fast convergence and high prediction accuracy, as well as the maintenance of nonnegativity constraints. Moreover, it is simple and easy to implement for real applications of learning systems.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/5fe3921038d76a4681428fd9d7f24d1f69c1e490

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Mengchu Zhou
2. Xin Luo
3. Ye Yuan
4. Sili Chen
5. Hao Wu
6. Zidong Wang
7. Huaqiang Yuan
8. Nianyin Zeng

In Computer Vision and Pattern Recognition

1. Mengchu Zhou
2. Xin Luo
3. Hao Wu
4. Zidong Wang
5. Huaqiang Yuan
6. Nianyin Zeng

In Information Systems

1. Xin Luo
2. Mengchu Zhou
3. Ye Yuan
4. Sili Chen
5. Hao Wu
6. Zidong Wang
7. Huaqiang Yuan
8. Nianyin Zeng

Citations in the corpus

• Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors (2020, methodology): “As demonstrated in our prior research [5], [24], [46], a learning model’s convergence rate can be improved by incorporating ADM into its training process with the following procedure: 1) decomposing the original task into multiple subtasks and 2) solving each subtask sequentially, where the…”
• Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, methodology): “For a tested model, root mean squared error (RMSE) and mean absolute error (MAE) are frequently used to validate a tested model’s prediction accuracy for missing data of an HiDS matrix [4, 12, 13, 16, 21, 22, 47, 48]: where Γ denotes the testing set, which is disjoint with Λ and Ω .”
• Artificial intelligence in recommender systems (2020, unknown): “Second, the factorization process makes a dense rating matrix, so that the sparsity problem can be alleviated [22].”