The Experts below are selected from a list of 57210 Experts worldwide ranked by ideXlab platform
Xin Luo - One of the best experts on this subject based on the ideXlab platform.
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a deep Latent Factor model for high dimensional and sparse matrices in recommender systems
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Mingsheng Shang, Guoyin Wang, Mengchu ZhouAbstract: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.
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an instance frequency weighted regularization scheme for non negative Latent Factor analysis on high dimensional and sparse data
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Zidong Wang, Mingsheng ShangAbstract:High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly seen in industrial applications, such as recommender systems. They can be modeled into an HiDS matrix, from which non-negative Latent Factor analysis (NLFA) is highly effective in extracting useful features. Preforming NLFA on an HiDS matrix is ill-posed, desiring an effective regularization scheme for avoiding overfitting. Current models mostly adopt a standard ${L} _{2}$ scheme, which does not consider the imbalanced distribution of known data in an HiDS matrix. From this point of view, this paper proposes an instance-frequency-weighted regularization (IR) scheme for NLFA on HiDS data. It specifies the regularization effects on each Latent Factors with its relevant instance count, i.e., instance-frequency, which clearly describes the known data distribution of an HiDS matrix. By doing so, it achieves finely grained modeling of regularization effects. The experimental results on HiDS matrices from industrial applications demonstrate that compared with an ${L} _{2}$ scheme, an IR scheme enables a resultant model to achieve higher accuracy in missing data estimation of an HiDS matrix.
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an l and l norm oriented Latent Factor model for recommender systems
IEEE Transactions on Neural Networks, 2021Co-Authors: Mingsheng Shang, Xin Luo, Zidong WangAbstract:A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a Latent Factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L₂ norm-oriented one, which ignores target data's characteristics described by other metrics like an L₁ norm-oriented one. To investigate this issue, this article proposes an L₁-and-L₂-norm-oriented LF (L³F) model. It adopts twofold ideas: 1) aggregating L₁ norm's robustness and L₂ norm's stability to form its Loss and 2) adaptively adjusting weights of L₁ and L₂ norms in its Loss. By doing so, it achieves fine aggregation effects with L₁ norm-oriented Loss's robustness and L₂ norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L³F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.
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algorithms of unconstrained non negative Latent Factor analysis for recommender systems
IEEE Transactions on Big Data, 2021Co-Authors: Xin Luo, Zhigang Liu, Mengchu Zhou, Mingsheng ShangAbstract:Non-negativity is vital for a Latent Factor (LF)-based model to preserve the important feature of a high-dimensional and sparse (HiDS) matrix in recommender systems, i.e., none of its entries is negative. Current non-negative models rely on constraints-combined training schemes. However, they lack flexibility, scalability, or compatibility with general training schemes. This work aims to perform unconstrained non-negative Latent Factor analysis (UNLFA) on HiDS matrices. To do so, we innovatively transfer the non-negativity constraints from the decision parameters to the output LFs, and connect them through a single-element-dependent mapping function. Then we theoretically prove that by making a mapping function fulfill specific conditions, the resultant model is able to represent the original one precisely. We subsequently design highly efficient UNLFA algorithms for recommender systems. Experimental results on four industrial-size HiDS matrices demonstrate that compared with four state-of-the-art non-negative models, a UNLFA-based model obtains advantage in prediction accuracy for missing data and computational efficiency. Moreover, such high performance is achieved through its unconstrained training process which is compatible with various general training schemes, on the premise of fulfilling non-negativity constraints. Hence, UNLFA algorithms are highly valuable for industrial applications with the need of performing non-negative Latent Factor analysis on HiDS matrices.
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a proportional integral derivative incorporated stochastic gradient descent based Latent Factor analysis model
Neurocomputing, 2021Co-Authors: Ye Yuan, Xin Luo, Tao Ruan, Jia ChenAbstract:Abstract Large-scale relationships like user-item preferences in a recommender system are mostly described by a high-dimensional and sparse (HiDS) matrix. A Latent Factor analysis (LFA) model extracts useful knowledge from an HiDS matrix efficiently, where stochastic gradient descent (SGD) is frequently adopted as the learning algorithm. However, a standard SGD algorithm updates a decision parameter with the stochastic gradient on the instant loss only, without considering information described by prior updates. Hence, an SGD-based LFA model commonly consumes many iterations to converge, which greatly affects its practicability. On the other hand, a proportional-integral-derivative (PID) controller makes a learning model converge fast with the consideration of its historical errors from the initial state till the current moment. Motivated by this discovery, this paper proposes a P ID-incorporated S GD-based L FA (PSL) model. Its main idea is to rebuild the instant error on a single instance following the principle of PID, and then substitute this rebuilt error into an SGD algorithm for accelerating model convergence. Empirical studies on six widely-accepted HiDS matrices indicate that compared with state-of-the-art LFA models, a PSL model achieves significantly higher computational efficiency as well as highly competitive prediction accuracy for missing data of an HiDS matrix.
Mingsheng Shang - One of the best experts on this subject based on the ideXlab platform.
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non negative Latent Factor model based on β divergence for recommender systems
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Luo Xin, Zhigang Liu, Ye Yuan, Mengchu Zhou, Mingsheng ShangAbstract:Non-negative Latent Factor (NLF) models well represent high-dimensional and sparse (HiDS) matrices filled with non-negative data, which are frequently encountered in industrial applications like recommender systems. However, current NLF models mostly adopt Euclidean distance in their objective function, which represents a special case of a ${\boldsymbol{\beta }}$ -divergence function. Hence, it is highly desired to design a ${\boldsymbol{\beta }}$ -divergence-based NLF ( ${\boldsymbol{\beta }}$ -NLF) model that uses a ${\boldsymbol{\beta }}$ -divergence function, and investigate its performance in recommender systems as ${\boldsymbol{\beta }}$ varies. To do so, we first model ${\boldsymbol{\beta }}$ -NLF’s learning objective with a ${\boldsymbol{\beta }}$ -divergence function. Subsequently, we deduce a general single Latent Factor-dependent, non-negative and multiplicative update scheme for ${\boldsymbol{\beta }}$ -NLF, and then design an efficient ${\boldsymbol{\beta }}$ -NLF algorithm. The experimental results on HiDS matrices from industrial applications indicate that by carefully choosing the value of ${\boldsymbol{\beta }}$ , ${\boldsymbol{\beta }}$ -NLF outperforms an NLF model with Euclidean distance in terms of accuracy for missing data prediction without increasing computational time. The research outcomes show the necessity of using an optimal ${\boldsymbol{\beta }}$ -divergence function in order to achieve the best performance of an NLF model on HiDS matrices. Hence, the proposed model has both theoretical and application significance.
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a deep Latent Factor model for high dimensional and sparse matrices in recommender systems
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Mingsheng Shang, Guoyin Wang, Mengchu ZhouAbstract: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.
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an instance frequency weighted regularization scheme for non negative Latent Factor analysis on high dimensional and sparse data
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Zidong Wang, Mingsheng ShangAbstract:High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly seen in industrial applications, such as recommender systems. They can be modeled into an HiDS matrix, from which non-negative Latent Factor analysis (NLFA) is highly effective in extracting useful features. Preforming NLFA on an HiDS matrix is ill-posed, desiring an effective regularization scheme for avoiding overfitting. Current models mostly adopt a standard ${L} _{2}$ scheme, which does not consider the imbalanced distribution of known data in an HiDS matrix. From this point of view, this paper proposes an instance-frequency-weighted regularization (IR) scheme for NLFA on HiDS data. It specifies the regularization effects on each Latent Factors with its relevant instance count, i.e., instance-frequency, which clearly describes the known data distribution of an HiDS matrix. By doing so, it achieves finely grained modeling of regularization effects. The experimental results on HiDS matrices from industrial applications demonstrate that compared with an ${L} _{2}$ scheme, an IR scheme enables a resultant model to achieve higher accuracy in missing data estimation of an HiDS matrix.
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an l and l norm oriented Latent Factor model for recommender systems
IEEE Transactions on Neural Networks, 2021Co-Authors: Mingsheng Shang, Xin Luo, Zidong WangAbstract:A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a Latent Factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L₂ norm-oriented one, which ignores target data's characteristics described by other metrics like an L₁ norm-oriented one. To investigate this issue, this article proposes an L₁-and-L₂-norm-oriented LF (L³F) model. It adopts twofold ideas: 1) aggregating L₁ norm's robustness and L₂ norm's stability to form its Loss and 2) adaptively adjusting weights of L₁ and L₂ norms in its Loss. By doing so, it achieves fine aggregation effects with L₁ norm-oriented Loss's robustness and L₂ norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L³F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.
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algorithms of unconstrained non negative Latent Factor analysis for recommender systems
IEEE Transactions on Big Data, 2021Co-Authors: Xin Luo, Zhigang Liu, Mengchu Zhou, Mingsheng ShangAbstract:Non-negativity is vital for a Latent Factor (LF)-based model to preserve the important feature of a high-dimensional and sparse (HiDS) matrix in recommender systems, i.e., none of its entries is negative. Current non-negative models rely on constraints-combined training schemes. However, they lack flexibility, scalability, or compatibility with general training schemes. This work aims to perform unconstrained non-negative Latent Factor analysis (UNLFA) on HiDS matrices. To do so, we innovatively transfer the non-negativity constraints from the decision parameters to the output LFs, and connect them through a single-element-dependent mapping function. Then we theoretically prove that by making a mapping function fulfill specific conditions, the resultant model is able to represent the original one precisely. We subsequently design highly efficient UNLFA algorithms for recommender systems. Experimental results on four industrial-size HiDS matrices demonstrate that compared with four state-of-the-art non-negative models, a UNLFA-based model obtains advantage in prediction accuracy for missing data and computational efficiency. Moreover, such high performance is achieved through its unconstrained training process which is compatible with various general training schemes, on the premise of fulfilling non-negativity constraints. Hence, UNLFA algorithms are highly valuable for industrial applications with the need of performing non-negative Latent Factor analysis on HiDS matrices.
Zidong Wang - One of the best experts on this subject based on the ideXlab platform.
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an instance frequency weighted regularization scheme for non negative Latent Factor analysis on high dimensional and sparse data
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Zidong Wang, Mingsheng ShangAbstract:High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly seen in industrial applications, such as recommender systems. They can be modeled into an HiDS matrix, from which non-negative Latent Factor analysis (NLFA) is highly effective in extracting useful features. Preforming NLFA on an HiDS matrix is ill-posed, desiring an effective regularization scheme for avoiding overfitting. Current models mostly adopt a standard ${L} _{2}$ scheme, which does not consider the imbalanced distribution of known data in an HiDS matrix. From this point of view, this paper proposes an instance-frequency-weighted regularization (IR) scheme for NLFA on HiDS data. It specifies the regularization effects on each Latent Factors with its relevant instance count, i.e., instance-frequency, which clearly describes the known data distribution of an HiDS matrix. By doing so, it achieves finely grained modeling of regularization effects. The experimental results on HiDS matrices from industrial applications demonstrate that compared with an ${L} _{2}$ scheme, an IR scheme enables a resultant model to achieve higher accuracy in missing data estimation of an HiDS matrix.
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an l and l norm oriented Latent Factor model for recommender systems
IEEE Transactions on Neural Networks, 2021Co-Authors: Mingsheng Shang, Xin Luo, Zidong WangAbstract:A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a Latent Factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L₂ norm-oriented one, which ignores target data's characteristics described by other metrics like an L₁ norm-oriented one. To investigate this issue, this article proposes an L₁-and-L₂-norm-oriented LF (L³F) model. It adopts twofold ideas: 1) aggregating L₁ norm's robustness and L₂ norm's stability to form its Loss and 2) adaptively adjusting weights of L₁ and L₂ norms in its Loss. By doing so, it achieves fine aggregation effects with L₁ norm-oriented Loss's robustness and L₂ norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L³F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.
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a fast non negative Latent Factor model based on generalized momentum method
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Zhigang Liu, Mingsheng Shang, Zidong WangAbstract:Non-negative Latent Factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single Latent Factor-dependent, non-negative and multiplicative update (SLF-NMU) is an efficient algorithm for building an NLF model on an HiDS matrix, yet it suffers slow convergence. A momentum method is frequently adopted to accelerate a learning algorithm, but it is incompatible with those implicitly adopting gradients like SLF-NMU. To build a fast NLF (FNLF) model, we propose a generalized momentum method compatible with SLF-NMU. With it, we further propose a single Latent Factor-dependent non-negative, multiplicative and momentum-incorporated update algorithm, thereby achieving an FNLF model. Empirical studies on six HiDS matrices from industrial application indicate that an FNLF model outperforms an NLF model in terms of both convergence rate and prediction accuracy for missing data. Hence, compared with an NLF model, an FNLF model is more practical in industrial applications.
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position transitional particle swarm optimization incorporated Latent Factor analysis
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xin Luo, Ye Yuan, Sili Chen, Nianyin Zeng, Zidong WangAbstract:High-dimensional and sparse (HiDS) matrices are frequently found in various industrial applications. A Latent Factor analysis (LFA) model is commonly adopted to extract useful knowledge from an HiDS matrix, whose parameter training mostly relies on a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's learning rate is hard to tune in real applications, making it vital to implement its self-adaptation. To address this critical issue, this study firstly investigates the evolution process of a particle swarm optimization algorithm with care, and then proposes to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden, thereby innovatively achieving a novel position-transitional particle swarm optimization (P2SO) algorithm. It is subsequently adopted to implement a P2SO-based LFA (PLFA) model that builds a learning rate swarm applied to the same group of LFs. Thus, a PLFA model implements highly efficient learning rate adaptation as well as represents an HiDS matrix precisely. Experimental results on four HiDS matrices emerging from real applications demonstrate that compared with an SGD-based LFA model, a PLFA model no longer suffers from a tedious and expensive tuning process of its learning rate to achieve higher prediction accuracy for missing data.
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Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models.
IEEE transactions on neural networks and learning systems, 2020Co-Authors: Zhigang Liu, Xin Luo, Zidong WangAbstract:A single Latent Factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU) learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a high-dimensional and sparse (HiDS) matrix. However, convergence characteristics of such NLF models are never justified in theory. To address this issue, this study conducts rigorous convergence analysis for an SLF-NMU-based NLF model. The main idea is twofold: 1) proving that its learning objective keeps nonincreasing with its SLF-NMU-based learning rules via constructing specific auxiliary functions; and 2) proving that it converges to a stable equilibrium point with its SLF-NMU-based learning rules via analyzing the Karush-Kuhn-Tucker (KKT) conditions of its learning objective. Experimental results on ten HiDS matrices from real applications provide numerical evidence that indicates the correctness of the achieved proof.
Mengchu Zhou - One of the best experts on this subject based on the ideXlab platform.
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non negative Latent Factor model based on β divergence for recommender systems
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Luo Xin, Zhigang Liu, Ye Yuan, Mengchu Zhou, Mingsheng ShangAbstract:Non-negative Latent Factor (NLF) models well represent high-dimensional and sparse (HiDS) matrices filled with non-negative data, which are frequently encountered in industrial applications like recommender systems. However, current NLF models mostly adopt Euclidean distance in their objective function, which represents a special case of a ${\boldsymbol{\beta }}$ -divergence function. Hence, it is highly desired to design a ${\boldsymbol{\beta }}$ -divergence-based NLF ( ${\boldsymbol{\beta }}$ -NLF) model that uses a ${\boldsymbol{\beta }}$ -divergence function, and investigate its performance in recommender systems as ${\boldsymbol{\beta }}$ varies. To do so, we first model ${\boldsymbol{\beta }}$ -NLF’s learning objective with a ${\boldsymbol{\beta }}$ -divergence function. Subsequently, we deduce a general single Latent Factor-dependent, non-negative and multiplicative update scheme for ${\boldsymbol{\beta }}$ -NLF, and then design an efficient ${\boldsymbol{\beta }}$ -NLF algorithm. The experimental results on HiDS matrices from industrial applications indicate that by carefully choosing the value of ${\boldsymbol{\beta }}$ , ${\boldsymbol{\beta }}$ -NLF outperforms an NLF model with Euclidean distance in terms of accuracy for missing data prediction without increasing computational time. The research outcomes show the necessity of using an optimal ${\boldsymbol{\beta }}$ -divergence function in order to achieve the best performance of an NLF model on HiDS matrices. Hence, the proposed model has both theoretical and application significance.
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a deep Latent Factor model for high dimensional and sparse matrices in recommender systems
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Mingsheng Shang, Guoyin Wang, Mengchu ZhouAbstract: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.
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algorithms of unconstrained non negative Latent Factor analysis for recommender systems
IEEE Transactions on Big Data, 2021Co-Authors: Xin Luo, Zhigang Liu, Mengchu Zhou, Mingsheng ShangAbstract:Non-negativity is vital for a Latent Factor (LF)-based model to preserve the important feature of a high-dimensional and sparse (HiDS) matrix in recommender systems, i.e., none of its entries is negative. Current non-negative models rely on constraints-combined training schemes. However, they lack flexibility, scalability, or compatibility with general training schemes. This work aims to perform unconstrained non-negative Latent Factor analysis (UNLFA) on HiDS matrices. To do so, we innovatively transfer the non-negativity constraints from the decision parameters to the output LFs, and connect them through a single-element-dependent mapping function. Then we theoretically prove that by making a mapping function fulfill specific conditions, the resultant model is able to represent the original one precisely. We subsequently design highly efficient UNLFA algorithms for recommender systems. Experimental results on four industrial-size HiDS matrices demonstrate that compared with four state-of-the-art non-negative models, a UNLFA-based model obtains advantage in prediction accuracy for missing data and computational efficiency. Moreover, such high performance is achieved through its unconstrained training process which is compatible with various general training schemes, on the premise of fulfilling non-negativity constraints. Hence, UNLFA algorithms are highly valuable for industrial applications with the need of performing non-negative Latent Factor analysis on HiDS matrices.
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Latent Factor based recommenders relying on extended stochastic gradient descent algorithms
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Mengchu Zhou, Dexian Wang, Huaqiang YuanAbstract:High-dimensional and sparse (HiDS) matrices generated by recommender systems contain rich knowledge regarding various desired patterns like users’ potential preferences and community tendency. Latent Factor (LF) analysis proves to be highly efficient in extracting such knowledge from an HiDS matrix efficiently. Stochastic gradient descent (SGD) is a highly efficient algorithm for building an LF model. However, current LF models mostly adopt a standard SGD algorithm. Can SGD be extended from various aspects in order to improve the resultant models’ convergence rate and prediction accuracy for missing data? Are such SGD extensions compatible with an LF model? To answer them, this paper carefully investigates eight extended SGD algorithms to propose eight novel LF models. Experimental results on two HiDS matrices generated by real recommender systems show that compared with an LF model with a standard SGD algorithm, an LF model with extended ones can achieve: 1) higher prediction accuracy for missing data; 2) faster convergence rate; and 3) model diversity.
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a regularization adaptive non negative Latent Factor analysis based model for recommender systems
2020 IEEE International Conference on Human-Machine Systems (ICHMS), 2020Co-Authors: Jiufang Chen, Xin Luo, Mengchu ZhouAbstract:Non-negative Latent Factor analysis (NLFA) can high-efficiently extract useful information from high dimensional and sparse (HiDS) matrices often encountered in recommender systems (RSs). However, an NLFA-based model requires careful tuning of regularization coefficients, which is highly expensive in both time and computation. To address this issue, this study proposes an adaptive NLFA-based model whose regularization coefficients become self-adaptive via particle swarm optimization. Experimental results on two HiDS matrices indicate that owing to such self-adaptation, it outperforms an NLFA model in terms of both convergence rate and prediction accuracy for missing data estimation.
Guoyin Wang - One of the best experts on this subject based on the ideXlab platform.
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a deep Latent Factor model for high dimensional and sparse matrices in recommender systems
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Xin Luo, Mingsheng Shang, Guoyin Wang, Mengchu ZhouAbstract: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.
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a data characteristic aware Latent Factor model for web services qos prediction
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xin Luo, Mingsheng Shang, Guoyin WangAbstract:How to accurately predict unknown quality-of-service (QoS) data based on observed ones is a hot yet thorny issue in Web service-related applications. Recently, a Latent Factor (LF) model has shown its efficiency in addressing this issue owing to its high accuracy and scalability. However, existing LF model-based QoS predictors mostly ignore the user/service neighborhoods, or reliability of given QoS data where noises commonly exist to cause accuracy loss. To address the above issues, this paper proposes a data-characteristic-aware Latent Factor (DCALF) model to implement highly accurate QoS predictions, where "data-characteristic-aware" indicates that it can appropriately implement QoS prediction according to the characteristics of given QoS data. Its main idea is two-fold: a) it detects the neighborhoods and noises of users and services based on the dense LFs extracted from the original sparse QoS data, b) it incorporates a density peaks-based clustering method into its modeling process for achieving the simultaneous detections of both neighborhoods and noises of QoS data. With such designs, it precisely represents the given QoS data in spite of their sparsity, thereby achieving highly accurate predictions for unknown ones. Results on two real datasets demonstrate that the proposed DCALF model outperforms state-of-the-art QoS predictors.
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a posterior neighborhood regularized Latent Factor model for highly accurate web service qos prediction
IEEE Transactions on Services Computing, 2020Co-Authors: Xin Luo, Mingsheng Shang, Guoyin WangAbstract:Neighborhood regularization is highly important for a Latent Factor (LF)-based Quality-of-Service (QoS)-predictor since similar users usually experience similar QoS when invoking similar services. Current neighborhood-regularized LF models rely prior information on neighborhood obtained from common raw QoS data or geographical information. The former suffers from low prediction accuracy due to the difficulty of constructing the neighborhood based on incomplete QoS data, while the latter requires additional geographical information that is usually difficult to collect considering information security, identity privacy, and commercial interests in real-world scenarios. To address the above issues, this work proposes a posterior-neighborhood-regularized LF (PLF) model for QoS prediction. The main idea is to decompose the LF analysis process into three phases: a) primal LF extraction, where the LFs are extracted to represent involved users/services based on known QoS data, b) posterior-neighborhood construction, where the neighborhood of each user/service is achieved based on similarities between their primal LF vectors, and c) posterior-neighborhood-regularized LF analysis, where the objective function is regularized by both the posterior-neighborhood of users/services and L2-norm of desired LFs. Experimental results from large scale QoS datasets demonstrate that PLF outperforms state-of-the-art models in terms of both accuracy and efficiency.
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a data aware Latent Factor model for web service qos prediction
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2019Co-Authors: Di Wu, Mingsheng Shang, Yi He, Guoyin Wang, Xindong WuAbstract:Accurately predicting unknown quality-of-service (QoS) data based on historical QoS records is vital in web service recommendation or selection. Recently, Latent Factor (LF) model has been widely and successfully applied to QoS prediction because it is accurate and scalable under many circumstances. Hence, state-of-the-art methods in QoS prediction are primarily based on LF. They improve the basic LF-based models by identifying the neighborhoods of QoS data based on some additional geographical information. However, the additional geographical information may be difficult to collect in considering information security, identity privacy, and commercial interests in real-world applications. Besides, they ignore the reliability of QoS data while unreliable ones are often mixed in. To address these issues, this paper proposes a data-aware Latent Factor (DALF) model to achieve highly accurate QoS prediction, where ‘data-aware’ means DALF can easily implement the predictions according to the characteristics of QoS data. The main idea is to incorporate a density peaks based clustering method into an LF model to discover the neighborhoods and unreliable ones of QoS data. Experimental results on two benchmark real-world web service QoS datasets demonstrate that DALF has better performance than the state-of-the-art models.
