Multi-task TSK fuzzy system modeling using inter-task correlation information

Jiang, Yizhang; Deng, Zhaohong; Chung, Fu-Lai; Wang, Shitong

doi:10.1016/j.ins.2014.12.007

Cited by 31 publications

(5 citation statements)

References 37 publications

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“…Motivated by DMF, we model dynamic selection data using a unified model that combines multi-task non-negative matrix factorization and a state transition matrix derived from LDS. Multi-task learning has become more popular because this method encourages learning tasks in parallel using a shared representation, which helps for each task learn better by using the other tasks’ information [ 24 – 26 ]. The multi-task NMF is a special case of non-negative tensor factorization (NTF) [ 27 , 28 ], which is more flexible and useful in practice because it attempts to estimate only one common factor in the form of a basis matrix and some coefficient matrices over time [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

Qian²,

et al. 2015

PLoS ONE

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Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

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Section: Introductionmentioning

confidence: 99%

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

Qian²,

et al. 2015

PLoS ONE

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“…However, with the "data governance" concept emerging in recent years, research about the open data ecosystem still has numerous issues that should be discussed. ese issues include the amount of influence that will be made by the subjective assumptions and other user psychological factors to the open data ecosystem, the use of other models, such as fuzzy systems [34] for researching open data ecosystems, and the amount of factors that will affect the sustainable development of the open data ecosystem, as well as the priorities and correlations among them. We should proceed next from the existing research to explore some effective strategies and ensure the sustainable development of the open data ecosystem in the future.…”

Section: Discussionmentioning

confidence: 99%

Information Diffusion between Users in Open Data Ecosystem: Modelling and Simulation Analysis

Tang

Zhu

Guo

2022

Mathematical Problems in Engineering

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Exploring the information interaction between users can provide a new way to solve open data ecosystem (ODE) sustainable development. In this paper, an information diffusion model, ILSOC, is proposed to analyze the information spreading among users within the ODE, and we use this model to inspect the impact on the sustainability of the ODE. First, we establish an information diffusion model that considers the public swing mentality. As a result, the relevant parameter variables are defined, and their influence is evaluated. Finally, this article proposes countermeasures to solve the sustainability problem within ODEs from the perspectives of users. Results demonstrate that (1) simply increasing the probability of interaction between users cannot effectively improve the sustainability of the ODE. (2) Parameters, such as contact rate, conversation rate, silent rate, and reversal rate, have constant effects on the ODE. (3) To maintain this ecosystem stability, some incentives and punishments for users in the public event are necessary.

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“…It is known that there are many ways to describe the system characteristic according to the observational data and expert knowledge, such as graph model (Hipel et al 2011), neural network model (Li et al 2016), fuzzy model (Jiang et al 2015). The graph model is composed of points and lines to describe the system structure and the causal relationships among variables.…”

Section: Construction Of Bayesian Causal Networkmentioning

confidence: 99%

Bayesian Causal Network for Discrete Variables

Jing

Zhou

Chen

2022

Intelligent Control and Learning Systems

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Ensuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.

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Multi-task TSK fuzzy system modeling using inter-task correlation information

Cited by 31 publications

References 37 publications

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

Information Diffusion between Users in Open Data Ecosystem: Modelling and Simulation Analysis

Bayesian Causal Network for Discrete Variables

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