A novel latent factor model for recommender system

Kumar, Bipul

doi:10.4301/s1807-17752016000300008

Cited by 10 publications

(8 citation statements)

References 2 publications

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“…The second major option relies on model-based variable completion, such as the ones presented in [ 28 , 50 ]. Most of these procedures consist of Singular Value Decomposition variants, commonly used in biological and medical applications.…”

Section: Methodsmentioning

confidence: 99%

Predicting special care during the COVID-19 pandemic: a machine learning approach

Bezzan

Rocco

2021

Health Inf Sci Syst

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More than ever, COVID-19 is putting pressure on health systems worldwide, especially in Brazil. In this study, we propose a method based on statistics and machine learning that uses blood lab exam data from patients to predict whether patients will require special care (hospitalization in regular or special-care units). We also predict the number of days the patients will stay under such care. The two-step procedure developed uses Bayesian Optimisation to select the best model among several candidates. This leads us to final models that achieve 0.94 area under ROC curve performance for the first target and 1.87 root mean squared error for the second target (which is a 77% improvement over the mean baseline)—making our model ready to be deployed as a decision system that could be available for everyone interested. The analytical approach can be used in other diseases and can help to plan hospital resources in other contexts.

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Section: Methodsmentioning

confidence: 99%

Predicting special care during the COVID-19 pandemic: a machine learning approach

Bezzan

Rocco

2021

Health Inf Sci Syst

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“…In fact, the FunkSVD algorithm transforms solving two optimal low-rank matrices into optimization problems. We need to define a loss function [14] such as (1), which is used to control the deviation of the model so that the gap between the predicted score and the actual score is as small as possible. Where T is the data set, x m is the user feature matrix-vector, and y n is the item feature matrix-vector.…”

Section: A Traditional Funksvd Algorithmmentioning

confidence: 99%

“…Where T is the data set, x m is the user feature matrix-vector, and y n is the item feature matrix-vector. However, when the FunkSVD algorithm is applied to the recommendation system, a regularization term is usually added to the loss function to control the variance of the model and prevent over-coupling, that is, to obtain a simple hidden factor vector [14]:…”

Section: A Traditional Funksvd 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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“…In order to evaluate accuracy, the RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) are popular metrics in RS domain research (Kumar, 2016). Since, RMSE gives more weightage to larger values of errors while MAE gives equal weightage to all values of errors, RMSE is preferred over MAE while evaluating the performance of RS.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Transfer Learning for Resolving Sparsity Problem in Recommender systems: Human Values Approach

Srivastava

Bala

Kumar

2017

JISTEM

Self Cite

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With the rapid rise in popularity of ecommerce application, Recommender Systems are being widely used by them to predict the response that a user will give to a given item. This prediction helps in cross selling, upselling and to increase the loyalty of their customers. However due to lack of sufficient feedback data these systems suffer from sparsity problem which leads to decline in their prediction efficiency. In this work, we have proposed and empirically demonstrated how the Transfer Learning approach using five dimensions of basic human values can be successfully used to alleviate the sparsity problem and increase the efficiency of recommender system algorithms.

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A novel latent factor model for recommender system

Cited by 10 publications

References 2 publications

Predicting special care during the COVID-19 pandemic: a machine learning approach

Predicting special care during the COVID-19 pandemic: a machine learning approach

Parallel Algorithm of Improved FunkSVD Based on GPU

Transfer Learning for Resolving Sparsity Problem in Recommender systems: Human Values Approach

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