scholarly journals User Response Prediction in Online Advertising

2021 ◽  
Vol 54 (3) ◽  
pp. 1-43
Author(s):  
Zhabiz Gharibshah ◽  
Xingquan Zhu
Keyword(s):  
Machine Learning ◽  
Mobile Apps ◽  
Online Advertising ◽  
Response Prediction ◽  
Online Marketing ◽  
Data Availability ◽  
Third Party ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Machine Learning Methods

Online advertising, as a vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions to products, purchases of items, or explicit user feedback through online surveys. Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction. However, existing literature mainly focuses on algorithmic-driven designs to solve specific challenges, and no comprehensive review exists to answer many important questions. What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction? How do we predict user response in a reliable and/or transparent way? In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications. Our essential goal is to provide a thorough understanding of online advertising platforms, stakeholders, data availability, and typical ways of user response prediction. We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focusing on the current progress of machine learning methods used in different online platforms. In addition, we also review applications of user response prediction, benchmark datasets, and open source codes in the field.

scholarly journals Modelling MTPL insurance claim events: Can machine learning methods overperform the traditional GLM approach?

2021 ◽  
Vol 4 (2) ◽  
pp. 34-69
Author(s):  
Dávid Burka ◽  
László Kovács ◽  
László Szepesváry
Keyword(s):  
Machine Learning ◽  
Network Models ◽  
Statistical Modelling ◽  
Third Party ◽  
Insurance Companies ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Long Time ◽  
Generalised Linear Modelling

Pricing an insurance product covering motor third-party liability is a major challenge for actuaries. Comprehensive statistical modelling and modern computational power are necessary to solve this problem. The generalised linear and additive modelling approaches have been widely used by insurance companies for a long time. Modelling with modern machine learning methods has recently started, but applying them properly with relevant features is a great issue for pricing experts. This study analyses the claim-causing probability by fitting generalised linear modelling, generalised additive modelling, random forest, and neural network models. Several evaluation measures are used to compare these techniques. The best model is a mixture of the base methods. The authors’ hypothesis about the existence of significant interactions between feature variables is proved by the models. A simplified classification and visualisation is performed on the final model, which can support tariff applications later.

scholarly journals Bootstrap Aggregating Approach to Short-Term Load Forecasting Using Meteorological Parameters for Demand Side Management in The North-Eastern Region of India

2021 ◽  
Author(s):  
Dipu Sarkar ◽  
Taliakum AO ◽  
Sravan Kumar Gunturi
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Meteorological Parameters ◽  
Load Forecasting ◽  
Data Availability ◽  
Production Costs ◽  
Eastern Region ◽  
Short Term ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland's load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that the Bagging methods outperform other models with respect to mean squared error and mean absolute percentage error.

scholarly journals MACHINE LEARNING: AN OVERVIEW

2021 ◽  
Vol 9 (7) ◽  
pp. 343-348
Author(s):  
Adya Trisal ◽  
Dheeraj Mandloi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Driven ◽  
Future Prospects ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Machine Learning Methods ◽  
Using Data ◽  
Computer Power

Given the tremendous availability of data and computer power, there is a resurgence of interest in using data driven machine learning methods to solve issues where traditional engineering solutions are hampered by modeling or algorithmic flaws. The purpose of this      article is to provide a comprehensive review of machine learning, including its history, types, applications, limitations and future prospects. In addition to this, the article also discusses the main point of difference between the field of artificial intelligence and machine learning.

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