AI-Driven Personalization in Retail: Transforming Customer Experience Through Intelligent Product Recommendations (Published)
This technical article explores the transformative impact of artificial intelligence on retail personalization, focusing on how advanced AI solutions like Amazon Personalize and fine-tuned language models are revolutionizing product recommendations and customer engagement. It examines a case study of an online fashion retailer that implemented a hybrid personalization system, combining recommendation algorithms with generative AI for dynamic content creation. The multi-layered architecture captures subtle behavioral signals, processes them through sophisticated recommendation engines, and delivers contextually relevant product suggestions with personalized descriptions. The article analyzes the significant business outcomes achieved through this implementation and details the technical considerations that organizations must address when building similar systems, including data pipeline architecture, model training strategies, privacy controls, and experimentation frameworks. The article concludes by exploring emerging frontiers in retail personalization technology, including multimodal recommendation systems that integrate visual and textual data, emotion-aware personalization that adapts to customer mood, and cross-channel personalization that creates consistent experiences across all touchpoints.
Keywords: Artificial Intelligence, Customer Experience, Recommendation systems, generative AI, retail personalization
Scaling AI Infrastructure: From Recommendation Engines to LLM Deployment with Paged Attention (Published)
This article explores the evolving landscape of AI infrastructure, tracing the architectural progression from traditional recommendation systems to modern large language model deployments. It demonstrates how personalization engines have transitioned from batch processing to real-time architectures while investigating the unique challenges posed by LLMs that necessitate specialized infrastructure solutions. The paper presents PagedAttention as implemented in vLLM, a novel approach addressing memory management challenges in transformer models through block-level allocation. By contrasting established recommendation pipelines with emerging LLMOps patterns, it provides insights into common infrastructure solutions that support experimentation, continuous training, and efficient inference across both domains, culminating in a practical implementation guide for serving LLaMA models.
Keywords: LLMOps, PagedAttention, Recommendation systems, inference optimization., machine learning infrastructure
An Intelligent Product Suggestion Algorithm Using Predictive Analysis for Personalized User Interface Building (Published)
The main objective of this research was to propose a technological solution to the long queues that are often seen in many retail outlets. As the solution this research proposes a self-checkout application. The application populates a list predicted next purchasing item set making the user interface intelligent and user friendly. The research introduces a model named RFR-U model to generate the next purchasing item list of the customer. It uses the parameters; relevance, recency and frequency to determine the next purchasing item set. The algorithm uses a rule based approach with weighted ratings. Although collaborative method is a popular method in finding such results, in the studied scenario, it is not applicable as the store does not maintain a comprehensive user profiles or facilitates the users to rate products. The proposed algorithm and the solution was evaluated both quantitatively and qualitatively and results show an accuracy above 80%.
Keywords: Frequency, Recency, Recommendation systems, Relevance, purchasing patterns, self-checkout