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Call for Papers - Artificial Intelligence Techniques for Consumer Behavior Analysis - AI in Business

Intelligent big data analytics combines big data, analytics, and artificial intelligence (AI). AI applications in data science, fintech, and marketing have significantly increased over the last five years. This has been fueled by reasons such as rising data availability in terms of volume, variety, and velocity, as well as advancements in hardware and software. AI is becoming an important business and analytic skill to alter corporate processes, reorganize workforces, optimize infrastructure, understand consumer behavior, and combine sectors. By 2021, according to Gartner, AI technology accounted for 30% of new revenue growth from industry-specific solutions. Understanding evolving consumer behavior has been a key research area for companies as they update their marketing and business strategies considering the rapid technological advancements. This Research Topic aims to present the developments in the use of AI techniques for understanding consumer behavior. There are a wide range of AI and machine learning (ML) algorithms and techniques that companies are using to understand consumer behavior. There are significant challenges in the use of AI and ML techniques for understanding consumer behavior, including selection biases, algorithmic flaws, and algorithmic unfairness that have to be investigated. The goal of this research topic is to provide an avenue for researchers intending to explore and use AI and ML techniques for understanding consumer behavior in various industries and sectors. Research topics of interest include but are not limited to the following: 1. Predictive Analysis Techniques 2. Neural Networks and Deep Learning Applications in Consumer behavior 3. Sentiment Analysis and Opinion Mining in social media 4. Natural Language Processing Techniques 5. Application of Knowledge-Based Methods 6. Consumer Behavior & Engagement 7. Consumer Behavior Analytics 8. Customer Experience and AI 9. Digital Marketing and Advertising 10. Privacy Issues in AI Analytics 11. Applying Machine Learning Techniques in Consumer Behavior Analytics 12. Probabilistic Graphical Models 13. Bayesian Networks 14. Probabilistic Relational Models (PRMs) 15. Stochastic Logic Programs (SLPs) 16. Bayesian Logic Programs (BLPs) 17. Relational Dependency Networks (RDN) 18. Relational Markov Networks (RMNs) 19. Markov Logic Networks (MLNs) 20. Applications of Statistical Relational Learning in Online Consumer Behavior 21. Learning from data with domain knowledge 22. Reinforcement learning and applications in Consumer Behavior Analytics 23. Autonomous learning and optimization systems 24. Privacy-preserving Statistical Relational Learning applications 25. Intelligent data analysis and application in Consumer Behavior Analytics 26. Data science techniques and application in Understanding Consumer Behavior

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