scholarly journals TrustRec: An effective approach to exploit implicit trust and distrust relationships along with explicitones for accurate recommendations

2020 ◽  
pp. 39-39
Author(s):  
Reyhani Hamedani ◽  
Irfan Ali ◽  
Jiwon Hong ◽  
Sang-Wook Kim
Keyword(s):  
Matrix Factorization ◽  
Computational Cost ◽  
Effective Solution ◽  
Trust Networks ◽  
Novel Approach ◽  
Factorization Model ◽  
Implicit Trust ◽  
The Matrix ◽  
Effectiveness And Efficiency ◽  
Single Matrix

Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks. In this paper, we point out the problems of existing trust-aware recommendation approaches as follows: (P1) exploiting sparse explicit trust and distrust relationships; (P2) considering a misleading assumption that a user pair having a trust/distrust relationship certainly has a similar/dissimilar preference in practice; (P3) employing the transitivity of distrust relationships. Then, we propose TrustRec, a novel approach based on the matrix factorization that provides an effective solution to each of the afore mentioned problems and incorporates all of them in a single matrix factorization model. Furthermore, TrustRec exploits only top-k most similar trustees and dissimilar distrustees of each user to improve both the computational cost and accuracy. The results of our extensive experiments demonstrate that TructRec outperforms existing approaches in terms of both effectiveness and efficiency.

scholarly journals Followee Recommendation in Microblog Using Matrix Factorization Model with Structural Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Yu ◽  
Robin G. Qiu
Keyword(s):  
Social Network ◽  
Matrix Factorization ◽  
Information Sources ◽  
Quality Information ◽  
High Quality ◽  
Structure Information ◽  
Factorization Model ◽  
Proposed Model ◽  
The Matrix ◽  
High Quality Information

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

Recommender System with Composite Social Trust Networks

2016 ◽  
Vol 13 (2) ◽  
pp. 56-73 ◽  
Author(s):  
Chaochao Chen ◽  
Xiaolin Zheng ◽  
Mengying Zhu ◽  
Litao Xiao
Keyword(s):  
Online Social Networks ◽  
Prediction Accuracy ◽  
Social Trust ◽  
Convergence Speed ◽  
Trust Network ◽  
True Value ◽  
Trust Networks ◽  
Factorization Model ◽  
Implicit Trust ◽  
Probabilistic Matrix Factorization

The development of online social networks has increased the importance of social recommendations. Social recommender systems are based on the idea that users who are linked in a social trust network tend to share similar interests. Thus, how to build an accurate social trust network will greatly affect recommendation performance. However, existing trust-based recommender approaches do not fully utilize social information to build rational trust networks and thus have low prediction accuracy and slow convergence speed. In this paper, the authors propose a composite trust-based probabilistic matrix factorization model, which is mainly composed of two steps: In step 1, the existing explicit trust network and the inferred implicit trust network are used to build a composite trust network. In step 2, the composite trust network is used to minimize both the rating difference and the trust difference between the true value and the inferred value. Experiments based on an Epinions dataset show that the authors' approach has significantly higher prediction accuracy and convergence speed than traditional collaborative filtering technology and the state-of-the-art trust-based recommendation approaches.

Attribute interaction aware matrix factorization method for recommendation

2021 ◽  
Vol 25 (5) ◽  
pp. 1115-1130
Author(s):  
Yongquan Wan ◽  
Lihua Zhu ◽  
Cairong Yan ◽  
Bofeng Zhang
Keyword(s):  
Parallel Computing ◽  
Matrix Factorization ◽  
Time Complexity ◽  
Optimization Strategy ◽  
Computing Technology ◽  
Training Time ◽  
Latent Vector ◽  
Factorization Model ◽  
The Matrix ◽  
Original Rating

Matrix factorization (MF) models are effective and easy to expand and are widely used in industry, such as rating prediction and item recommendation. The basic MF model is relatively simple. In practical applications, side information such as attributes or implicit feedback is often combined to improve accuracy by modifying the model and optimizing the algorithm. In this paper, we propose an attribute interaction-aware matrix factorization (AIMF) method for recommendation tasks. We partition the original rating matrix into different sub-matrices according to the attribute interactions, train each sub-matrix independently, and merge all the latent vectors to generate the final score. Since the generated sub-matrices vary in size, an adaptive regularization coefficient optimization strategy and an adaptive latent vector dimension optimization strategy are proposed for sub-matrix training, and a variety of latent vector merging methods are put forward. The method AIMF has two advantages. When the original rating matrix is particularly large, the training time complexity of the MF-based model becomes higher and the update cost of the model is also higher. In AIMF, because each sub-matrix is usually much smaller than the original rating matrix, the training time complexity is greatly reduced after using parallel computing technology. Secondly, in AIMF, it is not necessary to modify the matrix factorization model to incorporate attributes and their interactive information into the model to improve the performance. The experimental results on the two classic public datasets MovieLens 1M and MovieLens 100k show that AIMF can not only effectively improve the accuracy of recommendation, but also make full use of parallel computing technology to improve training efficiency without modifying the matrix factorization model.

scholarly journals Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wen Dai ◽  
Xi Liu ◽  
Yibo Gao ◽  
Lin Chen ◽  
Jianglong Song ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
New Drugs ◽  
Biological Information ◽  
Gene Interactions ◽  
Computational Techniques ◽  
Interaction Data ◽  
Factorization Model ◽  
The Matrix ◽  
Disease Associations ◽  
Novel Drug

There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space, which includes drug-gene interactions, disease-gene interactions, and gene-gene interactions, is aimed at providing molecular biological information for prediction of drug-disease associations. The rationality lies in our belief that association between drug and disease has its evidence in the interactome network of genes. Experiments show that the integration of genomic space is indeed effective. Drugs, diseases, and genes are described with feature vectors of the same dimension, which are retrieved from the interaction data. Then a matrix factorization model is set up to quantify the association between drugs and diseases. Finally, we use the matrix factorization model to predict novel indications for drugs.

scholarly journals A Low-Complexity Double EP-based Detector for Iterative Detection and Decoding in MIMO

2020 ◽  
Author(s):  
Juan José Murillo Fuentes ◽  
irene santos ◽  
José Carlos Aradillas ◽  
Matilde Sánchez-Fernández
Keyword(s):  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Computational Cost ◽  
Neumann Series ◽  
Low Complexity ◽  
Iterative Detection ◽  
Expectation Propagation ◽  
Novel Approach ◽  
Iterative Detection And Decoding ◽  
The Matrix

<div> <div> <div> <p>We propose a new iterative detection and decoding algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE), as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, i.e., the same as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases </p> </div> </div> </div>

scholarly journals An Efficient Tensor Completion Method Combining Matrix Factorization and Smoothness

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Leiming Tang ◽  
Xunjie Cao ◽  
Weiyang Chen ◽  
Changbo Ye
Keyword(s):  
Matrix Factorization ◽  
Low Complexity ◽  
Low Rank ◽  
Total Variation Regularization ◽  
Tensor Completion ◽  
Alternating Direction ◽  
Factorization Model ◽  
Efficiency And Effectiveness ◽  
The Matrix ◽  
Accelerate Convergence

In this paper, the low-complexity tensor completion (LTC) scheme is proposed to improve the efficiency of tensor completion. On one hand, the matrix factorization model is established for complexity reduction, which adopts the matrix factorization into the model of low-rank tensor completion. On the other hand, we introduce the smoothness by total variation regularization and framelet regularization to guarantee the completion performance. Accordingly, given the proposed smooth matrix factorization (SMF) model, an alternating direction method of multiple- (ADMM-) based solution is further proposed to realize the efficient and effective tensor completion. Additionally, we employ a novel tensor initialization approach to accelerate convergence speed. Finally, simulation results are presented to confirm the system gain of the proposed LTC scheme in both efficiency and effectiveness.

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches

2020 ◽  
Author(s):  
Sina Abdollahi ◽  
Peng-Chan Lin ◽  
Meng-Ru Shen ◽  
Jung-Hsien Chiang
Keyword(s):  
Clinical Data ◽  
Matrix Factorization ◽  
High Accuracy ◽  
Vast Number ◽  
Variant Of Uncertain Significance ◽  
Factorization Model ◽  
Latent Space ◽  
Uncertain Significance ◽  
Space Matrix ◽  
Single Matrix

Abstract Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease–gene associations, scholars face two essential challenges, namely the vast number of existing variants and the existence of variants which are recognized as variant of uncertain significance (VUS). To tackle these challenges, we propose algorithms to assign a significance to each gene rather than each variant, describing its degree of pathogenicity. Since the interpreters identified most of the variants as VUS, most of the gene scores were identified as uncertain significance. To predict the uncertain significance scores, we design two matrix factorization-based models: the common latent space model uses genomics variant data as well as heterogeneous clinical data, while the single-matrix factorization model can be used when heterogeneous clinical data are unavailable. We have managed to show that the models successfully predict the uncertain significance scores with low error and high accuracy. Moreover, to evaluate the effectiveness of our novel input features, we train five different multi-label classifiers including a feedforward neural network with the same feature set and show they all achieve high accuracy as the main impact of our approach comes from the features. Availability: The source code is freely available at https://github.com/sabdollahi/CoLaSpSMFM.

scholarly journals Novel Approach of Neural Collaborative Filter by Pairwise Objective Function with Matrix Factorization

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1213
Author(s):  
Ram Sethuraman ◽  
Akshay Havalgi
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Cross Entropy ◽  
Optimization Approach ◽  
Novel Approach ◽  
Latent Features ◽  
The Matrix

The concept of deep learning is used in the various fields like text, speech and vision. The proposed work deep neural network is used for recommender system. In this work pair wise objective function is used for emphasis of non-linearity and latent features. The GMF (Gaussian matrix factorization) and MLP techniques are used in this work. The proposed framework is named as NCF which is basically neural network based collaborative filtering. The NCF gives the latent features by reducing the non-linearity and generalizing the matrix. In the proposed work combination of pair-wise and point wise objective function is used and tune by using the concept of cross entropy with Adam optimization. This optimization approach optimizes the gradient descent function. The work is done on 1K and 1M movies lens dataset and it is compared with deep matrix factorization (DMF).  

scholarly journals A Low-Complexity Double EP-based Detector for Iterative Detection and Decoding in MIMO

2020 ◽  
Author(s):  
Juan José Murillo Fuentes ◽  
irene santos ◽  
José Carlos Aradillas ◽  
Matilde Sánchez-Fernández
Keyword(s):  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Computational Cost ◽  
Neumann Series ◽  
Low Complexity ◽  
Iterative Detection ◽  
Expectation Propagation ◽  
Novel Approach ◽  
Iterative Detection And Decoding ◽  
The Matrix

<div> <div> <div> <p>We propose a new iterative detection and decoding algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE), as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, i.e., the same as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases </p> </div> </div> </div>

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