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McKelvey School of Engineering

Data Mining Techniques: Recommendations through Matrix Factorization

Friday, April 8 | 11:00 AM - 12:00 PM

Henry A. and Elvira H. Jubel Hall, 121

Cynthia Ma

PhD Candidate

Washington University in St. Louis

Matrix factorization (MF) is exactly what it sounds like: the factorization of a matrix into a product of matrices. In Data Mining, implementations of MF like principal component analysis (PCA) have long been used for dimension reduction and exploratory analysis. More recently, MF has also become popular for training recommender systems, such as those used to produce movie suggestions to Netflix subscribers. In this talk, I'll first discuss the applications and differences of the PCA, ICA, and NMF methods. I'll then use the example of Netflix recommendations to introduce the challenge of applying MF to datasets with high sparsity, and how MF can be used to identify good recommendations by filling in missing values in the sparse data matrix.

Event Type



McKelvey School of Engineering


Science & Technology

Computer Science & Engineering
Event Contact


Speaker Information

Cynthia Ma is a PhD candidate for the Department of Computer Science and Engineering at Washington University in St. Louis. She is researching under Dr. Michael Brent. Her current research focuses on transcription factor activity inference from gene expression and regulation data using a non-negative matrix factorization model.

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