Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

Luo, Xin; Wen, Qin; Dong, Ani; Sedraoui, Khaled; Zhou, MengChu

doi:10.1109/jas.2020.1003396

Cited by 123 publications

(14 citation statements)

References 45 publications

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“…Recommendation systems can help users find useful information in the huge amount of information and recommend items of interest to users. Because of these advantages, it has been favored by researchers in industry and academia [20,21]. With the continuous improvement of computer algorithms, the development of the recommendation system is mainly divided into two stages: the traditional recommendation system and the deep learning-based recommendation system.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Social Recommendation System Based on Hypergraph Attention Network

Xia

Zhang

Weng

2021

Computational Intelligence and Neuroscience

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In recent years, due to the rise of online social platforms, social networks have more and more influence on our daily life, and social recommendation system has become one of the important research directions of recommendation system research. Because the graph structure in social networks and graph neural networks has strong representation capabilities, the application of graph neural networks in social recommendation systems has become more and more extensive, and it has also shown good results. Although graph neural networks have been successfully applied in social recommendation systems, their performance may still be limited in practical applications. The main reason is that they can only take advantage of pairs of user relations but cannot capture the higher-order relations between users. We propose a model that applies the hypergraph attention network to the social recommendation system (HASRE) to solve this problem. Specifically, we take the hypergraph’s ability to model high-order relations to capture high-order relations between users. However, because the influence of the users’ friends is different, we use the graph attention mechanism to capture the users’ attention to different friends and adaptively model selection information for the user. In order to verify the performance of the recommendation system, this paper carries out analysis experiments on three data sets related to the recommendation system. The experimental results show that HASRE outperforms the state-of-the-art method and can effectively improve the accuracy of recommendation.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Social Recommendation System Based on Hypergraph Attention Network

Xia

Zhang

Weng

2021

Computational Intelligence and Neuroscience

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“…The traditional gradient descent method uses the same learning rate for all parameters, so such a learning rate cannot adapt to the characteristics of all data, resulting in iterative oscillations and preventing convergence to the optimal solution. The so-called iterative oscillation means that each update may not proceed in the normal direction, and the parameter update has high variance, which causes the loss function to fluctuate sharply [18] [19].…”

Section: B Rmsprop Algorithmmentioning

confidence: 99%

Parallel Algorithm of Improved FunkSVD Based on GPU

Xiaochen

Liu

2022

IEEE Access

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Nowadays, the increasing amount of network data information and the development of big data technology have brought development opportunities and challenges to the recommendation system. The model-based collaborative filtering algorithm has become one of the mainstream algorithms in the recommendation system. For example, the Funk Singular Value Decomposition (FunkSVD) algorithm. However, in the face of big data calculations, data sparseness and iterative oscillations often affect the accuracy of the FunkSVD algorithm. Moreover, when the data volume is in units of GB or more, the FunkSVD algorithm runs slowly and is not effective. Therefore, we propose an improved FunkSVD algorithm (IFABG) based on RMSProp (Root Mean Square Prop) and GPU (Graphics Processing Unit) to solve this problem. Firstly, we use RMSProp algorithm to improve the traditional FunkSVD algorithm, alleviate data sparseness and iterative shock, and improve the prediction accuracy of the algorithm. Next, we implemented the parallelization of the improved FunkSVD algorithm in the GPU, which increased the calculation speed of the algorithm. Finally, we verify the IFABG algorithm under the Movielens dataset. The experimental results show that the IFABG algorithm is very suitable for processing sparse data, and it alleviates the iterative shock, and the prediction accuracy rate is about 30% higher than that of the traditional FunkSVD algorithm. The experimental results also show that the IFABG algorithm has a good acceleration effect. Under the same size data set, the IFABG algorithm is faster than the traditional FunkSVD, and the acceleration ratio can be as high as 19.27.

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“…As shown in Table 2, five popular datasets of industrial HDI matrices, including Douban, MovieLlens 20 M (ML20M), Flixter, Yahoo-R2, and Epinion [39][40][41][42] are used in this paper. Detailed information is described as follows:…”

Section: Datasetsmentioning

confidence: 99%

An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis

Pang

Shang

2022

J Big Data

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A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis (ADMA) is proposed to address these problems. First, a periodic equilibrium mechanism is employed using the physical mechanism annealing, which is embedded in the mutation operation of differential evolution (DE). Then, to further improve its efficiency, we adopt a probabilistic evaluation mechanism consistent with the crossover probability of DE. Experimental results of both adaptive and non-adaptive state-of-the-art methods on industrial HDI datasets illustrate that ADMA achieves a desirable global optimum with reasonable overhead and prevails competing methods in terms of predicting the missing data in HDI matrices.

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

Cited by 123 publications

References 45 publications

[Retracted] Social Recommendation System Based on Hypergraph Attention Network

[Retracted] Social Recommendation System Based on Hypergraph Attention Network

Parallel Algorithm of Improved FunkSVD Based on GPU

An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis

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