Journal ArticleDOI
A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems
TLDR
A deep latent factor model (DLFM) is proposed for building a deep-structured RS on an HiDS matrix efficiently by sequentially connecting multiple latent factor (LF) models instead of multilayered neural networks through a nonlinear activation function.Abstract:
Recommender systems (RSs) commonly adopt a user-item rating matrix to describe users’ preferences on items. With users and items exploding, such a matrix is usually high-dimensional and sparse (HiDS). Recently, the idea of deep learning has been applied to RSs. However, current deep-structured RSs suffer from high computational complexity. Enlightened by the idea of deep forest, this paper proposes a deep latent factor model (DLFM) for building a deep-structured RS on an HiDS matrix efficiently. Its main idea is to construct a deep-structured model by sequentially connecting multiple latent factor (LF) models instead of multilayered neural networks through a nonlinear activation function. Thus, the computational complexity grows linearly with its layer count, which is easy to resolve in practice. The experimental results on four HiDS matrices from industrial RSs demonstrate that when compared with state-of-the-art LF models and deep-structured RSs, DLFM can well balance the prediction accuracy and computational efficiency, which well fits the desire of industrial RSs for fast and right recommendations.read more
Citations
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Journal ArticleDOI
A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection
Di Wu,Yi He,Xin Luo,MengChu Zhou +3 more
TL;DR: This study proposes a latent-factor-analysis-based online sparse-streaming-feature selection algorithm (LOSSA), which is to apply latent factor analysis to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently.
Journal ArticleDOI
Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data
TL;DR: Zhang et al. as discussed by the authors proposed a smooth $L 1 -norm-oriented latent factor (SL-LF) model, which is more robust to outlier data.
Journal ArticleDOI
A Novel Group Recommendation Model With Two-Stage Deep Learning
TL;DR: A novel model, called group recommendation model with two-stage deep learning (GRMTDL), which can effectively absorb knowledge of user preferences into the process of GPL and design a novel layered transfer learning (LTL) method to learn group preferences by alternately optimizing these two subnetworks.
Journal ArticleDOI
An Overview of Recommendation Techniques and Their Applications in Healthcare
TL;DR: A comprehensive review of typical recommendation techniques and their applications in the field of healthcare is presented in this article, where an overview is provided on three famous recommendation techniques, namely, content-based, collaborative filtering (CF)-based, and hybrid methods.
Journal ArticleDOI
A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection
TL;DR: Li et al. as mentioned in this paper proposed a latent factor analysis-based online sparse-streaming-feature selection algorithm (LOSSA) to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently.
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Journal ArticleDOI
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