Abstract
Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work proposes to build a new LF-based CF model via second-order optimization to achieve higher accuracy. We first investigate a Hessian-free optimization framework, and employ its principle to avoid direct usage of the Hessian matrix by computing its product with an arbitrary vector. We then propose the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process. Compared with LF models based on first-order optimization algorithms, experimental results on two industrial datasets show that the proposed one can offer higher prediction accuracy with reasonable computational efficiency. Hence, it is a promising model for implementing high-performance recommenders.
Original language | English (US) |
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Article number | 7120953 |
Pages (from-to) | 946-956 |
Number of pages | 11 |
Journal | IEEE Transactions on Industrial Informatics |
Volume | 11 |
Issue number | 4 |
DOIs | |
State | Published - Aug 1 2015 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- Collaborative-filtering
- Hessian-free Optimization
- Incomplete Matrices
- Latent Factor Model
- Recommender Systems
- Second-order Optimization