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Introduction to Matrix Factorization for Recommender Systems
Recommender systems aim to personalize the experience of user by suggest...
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SMURFF: a High-Performance Framework for Matrix Factorization
Bayesian Matrix Factorization (BMF) is a powerful technique for recommen...
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Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation ...
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On Application of Block Kaczmarz Methods in Matrix Factorization
Matrix factorization techniques compute low-rank product approximations ...
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Feature-Based Matrix Factorization
Recommender system has been more and more popular and widely used in man...
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Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable da...
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Expanded Alternating Optimization of Nonconvex Functions with Applications to Matrix Factorization and Penalized Regression
We propose a general technique for improving alternating optimization (A...
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MatRec: Matrix Factorization for Highly Skewed Dataset
Recommender systems is one of the most successful AI technologies applied in the internet cooperations. Popular internet products such as TikTok, Amazon, and YouTube have all integrated recommender systems as their core product feature. Although recommender systems have received great success, it is well known for highly skewed datasets, engineers and researchers need to adjust their methods to tackle the specific problem to yield good results. Inability to deal with highly skewed dataset usually generates hard computational problems for big data clusters and unsatisfactory results for customers. In this paper, we propose a new algorithm solving the problem in the framework of matrix factorization. We model the data skewness factors in the theoretic modeling of the approach with easy to interpret and easy to implement formulas. We prove in experiments our method generates comparably favorite results with popular recommender system algorithms such as Learning to Rank , Alternating Least Squares and Deep Matrix Factorization.
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