An effective distributed predictive model with Matrix factorization and random forest for Big Data recommendation systems

Hammou, Badr Ait; Lahcen, Ayoub Ait; Mouline, Salma

doi:10.1016/j.eswa.2019.06.046

Cited by 31 publications

(16 citation statements)

References 60 publications

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“…They integrate several weak classification decision trees into a strong classifier. More specifically, each decision tree generates an independent classification and the final result is the one that received the majority of the votes among all the decision trees [62,63].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Ribeiro¹,

Carmo²,

Endo³

et al. 2022

Preprint

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Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Ribeiro¹,

Carmo²,

Endo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For a classification task, samples to be classified are used as inputs and each decision tree generates an independent classification result. The final classification labels are determined by the majority voting of all the decision trees [30], [37], [38]. Therefore, the RF algorithm can overcome some limitations of single decision tree and has been widely applied in the load forecasting [1], [39].…”

Section: B Load Consumption Pattern Determination Based On Random Forestmentioning

confidence: 99%

A Hybrid Short-Term Load Forecasting Model Based on Improved Fuzzy C-Means Clustering, Random Forest and Deep Neural Networks

et al. 2021

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Short-term load forecasting (STLF) plays an important role in the secure and reliable operation of the electric power system. Grouping similar load profiles by a clustering algorithm is a common method to reduce the uncertainty of electric consumption data. However, due to the uneven distribution of different date types in a historical data set, the tradition fuzzy c-means clustering (FCM) algorithm cannot identify typical load consumption patterns accurately. To solve this problem, a novel STLF model based on the improved FCM (IFCM) algorithm, random forest (RF) and deep neural networks (DNN) is proposed in this paper. First, IFCM is used to partition the load consumption profiles into several groups, and each group represents a typical load consumption pattern. The optimal number of clusters is determined by a recent clustering validity index. Then, a RF model is trained by the meteorological and calendar features of the historical data set. Finally, a DNN model is established for each group, and is trained using the features of the days that are partition into this group by IFCM. The experimental results on two daily load consumption data sets have showed that the proposed STLF model achieves better prediction performance as compared to other methods. In addition, the load consumption pattern of holidays was extracted from the historical data sets by utilizing IFCM, and the prediction performance of holidays in the testing set therefore has been significantly improved. INDEX TERMS clustering, fuzzy c-mean algorithm, load forecasting, random forest, deep neural network List of Abbreviations FCM, fuzzy c-means clustering STLF, short-term load forecasting RF, random forest DNN, deep neural network CNN, convolutional neural network RNN, recurrent neural network DBN, deep brief network LSTM, long short-term memory GRNN, generalized regression neural network ELM, extreme learning machine MAPE, mean absolute percentage error MASE, mean absolute scaled error RMSE, root mean square error SVM, support vector machine

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“…In preprocessing the review data, we attempt to clean our review data as much as possible using the natural language toolkit (NLTK) 2 in Python. The idea is to remove the punctuation, numbers and special characters in one step using regex replace, which will replace everything, except letters, with a space.…”

Section: B Review Data Preprocessingmentioning

confidence: 99%

“…The operation of missing rating prediction in the user-item interaction will be drastically reduced if the data are limited. The sparse data leads to generate potentially untrustworthy predictions [2], [35]. Although CF models can effectively predict temporal rating data exist, their capability of tracking the dynamics of user preferences is limited on sparse data, and there are few dynamic CF models that both consider drift on the basis of users' preferences and handle data sparsity [53], [60].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Wangwatcharakul

Wongthanavasu

2020

IEEE Access

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Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.

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An effective distributed predictive model with Matrix factorization and random forest for Big Data recommendation systems

Cited by 31 publications

References 60 publications

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

A Hybrid Short-Term Load Forecasting Model Based on Improved Fuzzy C-Means Clustering, Random Forest and Deep Neural Networks

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

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