Localized matrix factorization for recommendation based on matrix block diagonal forms

By Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma, Shi Feng. 2013

78 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Information Systems

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. Yongfeng Zhang
2. Aram Galstyan
3. Tat-seng Chua
4. Maarten de Rijke
5. Greg Ver Steeg
6. Junming Yin
7. Dong Guo
8. Jingkuan Song

In Electrical and Electronic Engineering

1. Yongfeng Zhang
2. Aram Galstyan
3. Tat-seng Chua
4. Zheng Qin
5. Greg Ver Steeg
6. Guokun Lai
7. Jingkuan Song
8. Xu Chen

In Information Systems

1. Yongfeng Zhang
2. Xu Chen
3. Xiangnan He
4. Tat-seng Chua
5. Zheng Qin
6. Min Zhang
7. Yiqun Liu
8. Shaoping Ma

Citations in the corpus

• Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, methodology): “[53] propose a localized matrix factorization approach to address data sparsity and scalability by factorizing block diagonal form matrices.”
• Learning to Rank Features for Recommendation over Multiple Categories (2016, background): “Among these models, latent factor models [2, 5, 11, 20] have gained much attention from the research community and industry due to their good prediction accuracy on some benchmark datasets.”
• Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (2014, background): “Various approaches, including Bayesian inference [4], [5], multidimensional scaling (MDS) [6], matrix factorization [7], [8], [9], [10], [11], [12], and mixed-membership model [13], have been proposed to infer the static latent space representation of the observed network.”
• Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014, methodology): “Various MF algorithms have been proposed in different problem settings, such as Singular Value Decomposition (SVD) [14, 32], Non-negative Matrix Factorization (NMF) [15], Max-Margin Matrix Factorization (MMMF) [29], Probabilistic Matrix Factorization (PMF) [30], and Localized Matrix Factorization (L”
• Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation (2015, background): “ACM 978-1-4503-3317-7/15/02 ...$15.00. http://dx.doi.org/10.1145/2684822.2697033. tributed to the emerging of Personalized Recommender Systems (PRS) [12], which attempt to make personalized and targeted item recommendations to users on various platforms and devices.”
• Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables (2014, methodology): “Consider conducting regularized NMF on a simple Block Diagonal Form (BDF) [28, 29, 27] structured matrix X = [ X11 X22 ] , where the observed values are restricted in the diagonal blocks X11 and X22, and the ratings in offdiagonals X12 and X21 are all unobserved.”
• NAIS: Neural Attentive Item Similarity Model for Recommendation (2018, background): “Many variants to MF have been proposed, such as SVD++ [30], Localized MF [31], Hierarchical MF [32], Social-aware MF [26], and Cross-Platform MF [33].”