Xin Luo
6 papers in the PaperMetrix corpus
Papers by this author
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Elastic net-regularized latent factor model for recommender systems
2018
Latent factor (LF) models are highly efficient in recommender systems. The problem of LF analysis is defined on high-dimensional and sparse (HiDS) matrices corresponding to relationships among numerous entities in industrial applications. It is ill-posed …
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Multi-criteria Group Decision-making Method Based on Expert Trust Network and Cloud Model
2021
Aiming at the fuzzy weight of experts, the complexity of objects and the fuzziness of human thinking in the process of multi-criteria group decision-making, this paper proposed to use expert trust network and cloud model …
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An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems
2015 · IEEE Transactions on Industrial Informatics
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 …
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Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
2015 · IEEE Transactions on Neural Networks and Learning Systems
Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work …
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A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method
2015 · IEEE Transactions on Neural Networks and Learning Systems
Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and …
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A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection
2021 · IEEE Transactions on Systems Man and Cybernetics Systems
Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does …