Regularized Singular Value Decomposition and Application to Recommender System
Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc. In recent applications, regularization becomes an increasing trend. In this paper, we present a regularized SVD (RSVD), present an efficient computational algorithm, and provide several theoretical analysis. We show that although RSVD is non-convex, it has a closed-form global optimal solution. Finally, we apply RSVD to the application of recommender system and experimental result show that RSVD outperforms SVD significantly.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningRecommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Enhancing Group Recommendation using Soft Impute Singular Value Decomposition
The growing popularity of group activities increased the need to develop methods for providing recommendations to a group of users based on the collective preferences of the group members. Several group recommender syste…
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not early stopped, or optimized via a regulariz…
CT ReconstructionImage ReconstructionProjection techniques to update the truncated SVD of evolving matrices
This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such matrix problems represent an important c…
Recommendation SystemsVariational Quantum Singular Value Decomposition
Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a giv…
Image CompressionRecommendation SystemsBlock based Singular Value Decomposition approach to matrix factorization for recommender systems
With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems. Producing high quality recommendations with scalability and performance is the need of the hour. Singular V…
GPURecommendation Systems