A Novel Technique for Behavioral Analytics Using Ensemble Learning Algorithms in E-Commerce

The era of E-commerce and availability of data in every field of operations in an enormous volume that implies to Big Data is one of the biggest sources of competitive advantage for the organizations in this digital world. It provides useful information to grow businesses by posting the advertisement and help consumers find the relevant product according to their preferences. The focus of this research is on the advertisement strategies analysis under which a Business employs several online advertisement strategies in order to appeal to the consumer. This research work will present a detailed analysis in user behavioral to use for business or Online Behavioral advertising and provide the framework of how Enterprise Resource Planning systems track the targeted audience and show their content. The paper’s prime objective is to classify and effectively run targeted advertising using the data that shows user’s retail behavior. This is where an Enterprise Resource Planning driven data will give rise to behavioral analytics. In addition to this, various data streaming technologies are also emphasized that will help to create a pipeline for the huge amount of data in Enterprise Resource Planning’s database.

[1]  Taghi M. Khoshgoftaar,et al.  RUDY Attack: Detection at the Network Level and Its Important Features , 2016, FLAIRS.

[2]  Sanne Kruikemeier,et al.  Behavioral Advertising : A Literature Review and Research Agenda , 2017 .

[3]  Tsan-Ming Choi,et al.  Advertising Strategies for Mobile Platforms With “Apps” , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  托马斯·A·杰瑞斯,et al.  Computer method and apparatus for targeting advertising , 2006 .

[5]  Ali Hamzeh,et al.  A Lazy Ensemble Learning Method to Classification , 2010 .

[6]  H. Beales,et al.  The Value of Behavioral Targeting , 2010 .

[7]  D. Mahr,et al.  Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness , 2015 .

[8]  Zhijun Ding,et al.  Behavior Analysis for Electronic Commerce Trading Systems: A Survey , 2019, IEEE Access.

[9]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[10]  Raymond J. Mooney,et al.  Diverse ensembles for active learning , 2004, ICML.

[11]  Xiaojun Wang,et al.  Technology in the 21st century: New challenges and opportunities , 2019, Technological Forecasting and Social Change.

[12]  Maureen K. Ohlhausen,et al.  Privacy Challenges and Opportunities: The Role of the Federal Trade Commission , 2014 .

[13]  M. S. Prasad Babu,et al.  Big data and predictive analytics in ERP systems for automating decision making process , 2014, 2014 IEEE 5th International Conference on Software Engineering and Service Science.

[14]  Hilde A. M. Voorveld,et al.  Understanding online behavioural advertising: User knowledge, privacy concerns and online coping behaviour in Europe , 2014, Comput. Hum. Behav..

[15]  Yongxin Yang,et al.  Deep Neural Decision Trees , 2018, ArXiv.

[16]  Anders Haug,et al.  A classification model of ERP system data quality , 2009, Ind. Manag. Data Syst..

[17]  MengChu Zhou,et al.  Sociability-based Influence Diffusion Probability Model to evaluate influence of BBS post , 2018, Neurocomputing.

[18]  REGULATION (EU) 2019/518 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL , 2015 .

[19]  S. Poornima,et al.  A survey of predictive analytics using big data with data mining , 2018, Int. J. Bioinform. Res. Appl..

[20]  Dora Kwei Arko.  Successful Strategies for Energy Sector Enterprise Resource Planning Projects , 2019 .

[21]  MengChu Zhou,et al.  Online Change-Point Detection in Sparse Time Series With Application to Online Advertising , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Hong-Ning Dai,et al.  A Rhombic Dodecahedron Topology for Human-Centric Banking Big Data , 2019, IEEE Transactions on Computational Social Systems.

[23]  Abraham J. Wyner,et al.  Modern Neural Networks Generalize on Small Data Sets , 2018, NeurIPS.

[24]  Michelle R. Nelson,et al.  The role of persuasion knowledge, assessment of benefit and harm, and third-person perception in coping with online behavioral advertising , 2016, Comput. Hum. Behav..

[25]  Jiujun Cheng,et al.  Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Jiawei Han,et al.  Machine Learning and Knowledge Discovery for Engineering Systems Health Management , 2011 .

[27]  Timos Sellis,et al.  Big data analytics in telecommunications: literature review and architecture recommendations , 2020, IEEE/CAA Journal of Automatica Sinica.

[28]  Komal Kumar Bhatia,et al.  OPINION TARGET EXTRACTION WITH SENTIMENT ANALYSIS , 2018, International Journal of Computing.

[29]  Nilanjan Dey,et al.  Internet of Things and Big Data Analytics Toward Next-Generation Intelligence , 2018 .

[30]  Kevin Lane Keller.  Unlocking the Power of Integrated Marketing Communications: How Integrated Is Your IMC Program? , 2016 .

[31]  Jan Stallaert,et al.  An Economic Analysis of Online Advertising Using Behavioral Targeting , 2010, MIS Q..

[32]  Chungang Yan,et al.  A behavioral authentication method for mobile based on browsing behaviors , 2020, IEEE/CAA Journal of Automatica Sinica.

[33]  Ahmed Elragal,et al.  Augmenting Advanced Analytics into Enterprise Systems: A Focus on Post-Implementation Activities , 2019, Syst..

[34]  Catherine Tucker,et al.  Social Networks, Personalized Advertising, and Privacy Controls , 2013 .

[35]  Malka N. Halgamuge,et al.  Review: Big Data Techniques of Google, Amazon, Facebook and Twitter , 2018, J. Commun..

[36]  Jennifer Jie Xu,et al.  Business Intelligence in Blogs: Understanding Consumer Interactions and Communities , 2012, MIS Q..

[37]  Abhishek Srivastava,et al.  Commonality-Rarity Score Computation: A novel Feature Selection Technique using Extended Feature Space of ELM for Text Classification , 2016, FIRE.

[38]  Terry Anthony Byrd,et al.  Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations , 2018 .

[39]  MengChu Zhou,et al.  Mobile Phone Data Analysis: A Spatial Exploration Toward Hotspot Detection , 2019, IEEE Transactions on Automation Science and Engineering.

[40]  Sung-Hyuk Park,et al.  A Social Network-Based Inference Model for Validating Customer Profile Data , 2012, MIS Q..

[41]  Komal Kumar Bhatia,et al.  Sentiment Analysis and Mining of Opinions , 2018 .

[42]  Jose Aguilar,et al.  An Adaptive Intelligent Management System of Advertising for Social Networks: A Case Study of Facebook , 2018, IEEE Transactions on Computational Social Systems.

[43]  Ahmed Abbasi,et al.  MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud , 2012, MIS Q..