Designing Retail Recommender Systems with Generative AI: Enhancing Customer Engagement and Sales

Abstract

Fast-growing AI has changed retail marketing and customer service. Customised sales and engagement increase using recommender systems. Collaboration and traditional content screening personalise users. Generative AI affects system creation and operation. Cutting-edge algorithms and generational AI may improve retail recommender systems by providing context-relevant, high-quality suggestions. Transformers, LLMs, and VAEs confuse users. 

Gen AI customises concepts. Content creation and user prediction. We study how generative models customise complicated user data. Discover data-driven synthesis methods. Multimodal learning uses text, images, and interactions. Generative AI may change concepts with human input. Beyond data-driven tendencies, clients may be surprised. 

Examples of generative AI and shop recommendations. Generators steal users, retention, and income from traditional systems. AI-powered fashion retail virtual shopping assistants design distinctive apparel. Real-time product recommendation systems in e-commerce combine user input and events. AI retail customisation may boost productivity and consumer satisfaction. 

The study explores Generative AI's recommendation system weaknesses. Large-scale generative model training is expensive and challenging, requires high-quality and vast datasets, and causes model bias from inadequate training data. Responsible governance that protects user data and is ethical is highlighted. Generative AI is promising but requires protection, says the research. Fairness audits and adversarial training reduce model bias. 

Generative AI recommendation systems must trade algorithm performance and readability. Basic generative AI models are examined without sacrificing developer and stakeholder results. Feature importance and attention visualisation tests confirm system principles. Openness builds customer trust in accommodating systems. AI-powered solutions gain trust.