Semi-discrete Matrix Factorization

Discrete matrix factorization (DMF) has been a promising solution to improve the inferring efficiency of matrix factorization (MF) against the rapidly growing numbers of users and items. However, DMF suffers from a serious encoding loss due to its oversimplified modeling on the original data geometry. In this article, we propose a semi-discrete matrix factorization (SDMF) model to combine the predicting efficacy of MF with the inferring efficiency of DMF. It first learns real-valued latent features by MF, and then, taking them as group-wise and point-wise smoothness, learns binary codes in the DMF framework, for preserving the geometrical structures collectively hidden in users and items, as well as aligning binary codes originated from Hamming space with their real-valued counterparts learned from vector space. Particularly, we devise a computationally efficient optimization algorithm to estimate model parameters. Extensive evaluations on three real-world datasets clearly demonstrate the superiority of our SDMF model over state-of-the-art hash-based recommendation methods.

Authors

• Jun Wu HiThink Research
• Fangyuan Luo Beijing Jiaotong University
• Yujia Zhang Shanxi University
• Haishuai Wang Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University, China

Keywords

• Binary codes
• Optimization
• Computational modeling
• Recommender systems
• Quantization (signal)
• Intelligent systems
• Geometry
• Data models
• Computational efficiency
• Matrix Factorization
• Smoothing
• Optimization Algorithm
• Vector Space
• Real-world Datasets
• Binary Code
• Latent Features
• Discrete Problem
• Latent Vector
• Two-stage Strategy
• Collaborative Filtering
• Matrix Factorization Model
• Recommendation Task
• Latent Space Vector
• Learning Process
• Time Complexity
• Regularization Term
• Iterative Convergence
• Loss Term
• Hash Function
• Two-stage Method
• Short Codes
• Random Initialization
• Reference Matrix
• Learning to Hash