A mobile recommendation system based on logistic regression and Gradient Boosting Decision Trees

Wang, Yaozheng; Feng, Dawei; Li, Dongsheng; Chen, Xinyuan; Zhao, Yunxiang; Niu, Xiamu

doi:10.1109/ijcnn.2016.7727431

Cited by 54 publications

(23 citation statements)

References 16 publications

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“…Because it does not require making assumptions on the data, it is extensively used in certain fields, such as in the optimization of recommendation systems [62,63], visual tracking algorithms [64], and traffic systems [65][66][67][68]. The attractiveness of GBRT comes from its ability to deal with the uneven distribution of data attributes, its lack of limitation for any hypothesis of input data, its better predictive capacity than a single decision tree, its power to deal with larger data size, and its transparency in terms of model development.…”

Section: Gradient Boosting Regression Treementioning

confidence: 99%

Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

et al. 2018

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Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R 2 value of 0.82 and a root mean square error (RMSE) value of 27.71 W·m −2 (38.38%). These values are 0.64 and 42.97 W·m −2 (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R 2 value of 0.92 and an RMSE of 15.40 W·m −2 (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.

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Section: Gradient Boosting Regression Treementioning

confidence: 99%

Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

et al. 2018

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“…Vector Machine (SVM), Random Forest Model (RF) and Gradient Boosting Regression Tree Model (GBDT) [25]. We also compare the products that have been visited for the last 8 h. Experimental tool is sklearn kit.…”

Section: Experimental Analysismentioning

confidence: 99%

Mobile marketing recommendation method based on user location feedback

Yin

Ding

Wang

2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionIn recent years, the e-commerce industry has developed rapidly with the popularization of the Internet. At this time, famous e-commerce platforms such as Alibaba and Amazon were born. E-commerce moved physical store products to a virtual network platform. On the one hand, it is convenient for users to buy various products without leaving the home. On the other hand, it is also convenient for sellers to sell their own goods and reduce costs. However, the various products have made it more difficult for users to select products. E-commerce platform can generate a large amount of user location feedback data which contains a wealth of user preference information [1]. It is significant to predict the location of the next consumer's consumption from these behavioral data. At present, most of the recommended methods focus on the user-product binary matrix and directly model their binary relationships [2]. The users' location information and shopping location information are considered as the third factor. In this case, you can only use the limited check-in data. The users' location feedback behavior and the timeliness of behavior are often overlooked. The mobile recommendation system takes advantage of the mobile network environment in terms of information recommendation and overcomes the disadvantages. Filtering irrelevant information by predicting potential mobile user preferences and providing Abstract Location-based mobile marketing recommendation has become one of the hot spots in e-commerce. The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of users and shopping location information in the recommendation. This paper focuses on location feedback data of user and proposes a location-based mobile marketing recommendation model by convolutional neural network (LBCNN). First, the users' location-based behaviors are divided into different time windows. For each window, the extractor achieves users' timing preference characteristics from different dimensions. Next, we use the convolutional model in the convolutional neural network model to train a classifier. The experimental results show that the model proposed in this paper is better than the traditional recommendation models in the terms of accuracy rate and recall rate, both of which increase nearly 10%.

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“…Furthermore, Gradient Boosting Decision Trees (GBDT) is an addictive ensemble regression model in decision trees. Wang et al [25] proposed a new fusion method based on the LR algorithm and GBDT algorithm for mobile recommendation system. Their method is observed to achieve a good F1 score in a mobile recommendation scenario.…”

Section: Ensemble Regression Modelmentioning

confidence: 99%

Coupled Kernel Ensemble Regression

Wornyo¹,

Ocquaye²,

Bediako-Kyeremeh³

2018

IJCA

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In this paper, the concept of kernel ensemble regression scheme is enhanced considering the absorption of multiple kernel regrssors into a unified ensemble regression framework simultaneously and coupled by minimizing total loss of ensembles in Reproducing kernel Hilbert Space. By this, one kernel regressor with more accurate fitting precession on data can automatically obtain bigger weight, which leads to a better overall ensemble performance. Comparing several single and ensemble regression methods such as Gradient Boosting, Support Vector Regression, Ridge Regression, Tree Regression and Random Forest with our proposed method, the experimental results of the proposed model indicates the highest performances in terms with regression and classification tasks using several UCI dataset.

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A mobile recommendation system based on logistic regression and Gradient Boosting Decision Trees

Cited by 54 publications

References 16 publications

Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

Mobile marketing recommendation method based on user location feedback

Coupled Kernel Ensemble Regression

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