Real-Time Data Processing Techniques for E-Commerce Personalization

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@article{1705264,
      author = {Bamigboye Kehinde},
      title = {Real-Time Data Processing Techniques for E-Commerce Personalization},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {361-375},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705264.pdf},
      abstract = {In today?s highly competitive e-commerce environment, personalization has become essential for enhancing customer experience and driving business growth. Real-time data processing enables platforms to analyze user behavior instantly, allowing them to provide personalized recommendations, targeted offers, and responsive customer service in real time. This article examines various real-time data processing techniques that facilitate personalization in e-commerce, covering key components such as data ingestion, processing frameworks, and data storage solutions. Additionally, we explore algorithms used in real-time recommendation systems, from collaborative and content-based filtering to advanced machine learning and deep learning models. Case studies of industry leaders such as Amazon and Netflix highlight the practical applications and benefits of these techniques. The article also addresses the challenges of scalability, latency, and data privacy associated with real-time personalization. Finally, future trends, including advancements in AI, privacy-preserving technologies, and the potential impact of quantum computing, are discussed, offering insights into the future of real-time personalization in e-commerce.},
      keywords = {Real-Time Data Processing, E-Commerce Personalization, Data Engineering, Machine Learning, Collaborative Filtering, Deep Learning, Scalability, Data Privacy, Customer Experience},
      month = {December},
  }