Source Apportionment of Total Suspended Particles (TSP) by Positive Matrix Factorization (PMF) and Chemical Mass Balance (CMB) Modeling in Ahvaz, Iran
Sign Up to like & getrecommendations! Published in 2018 at "Archives of Environmental Contamination and Toxicology"
DOI: 10.1007/s00244-017-0500-z
Abstract: There is a compelling need for apportionment of pollutants’ sources to facilitate their reduction through proper management plans. The present study was designed to determine the contribution of each possible source of total suspended particles… read more here.
Keywords: positive matrix; suspended particles; total suspended; cmb ... See more keywords
Neural embedding collaborative filtering for recommender systems
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DOI: 10.1007/s00521-020-04920-9
Abstract: The main purpose of collaborative filtering algorithm is to provide a personalized recommender system based on past interactions of each user (e.g., clicks and purchases). Among various collaborative filtering techniques, matrix factorization is widely adopted… read more here.
Keywords: collaborative filtering; recommender; neural embedding; matrix factorization ... See more keywords
Adaptive local learning regularized nonnegative matrix factorization for data clustering
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DOI: 10.1007/s10489-018-1380-2
Abstract: Data clustering aims to group the input data instances into certain clusters according to the high similarity to each other, and it could be regarded as a fundamental and essential immediate or intermediate task that… read more here.
Keywords: adaptive local; matrix; data clustering; matrix factorization ... See more keywords
Applying matrix factorization to consistency-based direct diagnosis
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DOI: 10.1007/s10489-020-02183-4
Abstract: Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often… read more here.
Keywords: matrix factorization; diagnosis; direct diagnosis; quality ... See more keywords
A hybrid recommendation approach using LDA and probabilistic matrix factorization
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DOI: 10.1007/s10586-018-1972-y
Abstract: Recommender systems provide users with suggestions and selections. Hybrid approaches which combine the neighborhood-based methods and the model-based methods have become popular when building collaborative filtering recommenders, but similarity is established between users/items only by… read more here.
Keywords: probabilistic matrix; matrix factorization; recommendation approach; approach ... See more keywords
A Deep Stochastic Model for Detecting Community in Complex Networks
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DOI: 10.1007/s10955-016-1681-y
Abstract: Discovering community structures is an important step to understanding the structure and dynamics of real-world networks in social science, biology and technology. In this paper, we develop a deep stochastic model based on non-negative matrix… read more here.
Keywords: community; stochastic model; model; matrix factorization ... See more keywords
A Model-Bias Matrix Factorization Approach for Course Score Prediction
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DOI: 10.1007/s11063-020-10385-7
Abstract: Recommender algorithms are widely used in e-commercial platforms to recommend users suitable items according to users’ preferences. In recent years, an increasing amount of attention has been paid to the application of recommender system in… read more here.
Keywords: course score; score prediction; course; matrix factorization ... See more keywords
Extended matrix factorization with entity network construction for recommendation
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DOI: 10.1007/s12652-021-03345-z
Abstract: In order to improve the performance of recommender systems, user social information and item attribute information should be integrated when building the prediction model, which is a hotspot and difficulty in the field of recommender… read more here.
Keywords: users items; recommendation; network; extended matrix ... See more keywords
An augmented Lagrangian alternating direction method for overlapping community detection based on symmetric nonnegative matrix factorization
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DOI: 10.1007/s13042-019-00980-z
Abstract: In this paper, we present an augmented Lagrangian alternating direction algorithm for symmetric nonnegative matrix factorization. The convergence of the algorithm is also proved in detail and strictly. Then we present a modified overlapping community… read more here.
Keywords: matrix factorization; community detection; symmetric nonnegative; nonnegative matrix ... See more keywords
Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization
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DOI: 10.1007/s40305-020-00322-9
Abstract: Orthogonal nonnegative matrix factorization (ONMF) is widely used in blind image separation problem, document classification, and human face recognition. The model of ONMF can be efficiently solved by the alternating direction method of multipliers and… read more here.
Keywords: orthogonal nonnegative; nonnegative matrix; matrix factorization; randomized algorithms ... See more keywords
Orthogonal incremental non-negative matrix factorization algorithm and its application in image classification
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DOI: 10.1007/s40314-020-1091-2
Abstract: To improve the sparseness of the base matrix in incremental non-negative matrix factorization, we in this paper present a new method, orthogonal incremental non-negative matrix factorization algorithm (OINMF), which combines the orthogonality constraint with incremental… read more here.
Keywords: negative matrix; matrix; incremental non; matrix factorization ... See more keywords