View Proposal

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Proposer

Lawrence John O'Brien

Title

Enhancing Market Basket Analysis with LLMS, SLMS, and Generative AI for Intelligent Business Recommendation Systems

Goal

This project aims to revolutionize traditional Market Basket Analysis (MBA) by integrating Large Language Models (LLMs), Small Language Models (SLMs), and Generative AI to provide advanced, context-aware business recommendations.

Description

This project aims to revolutionize traditional Market Basket Analysis (MBA) by integrating Large Language Models (LLMs), Small Language Models (SLMs), and Generative AI to provide advanced, context-aware business recommendations. Unlike conventional MBA that focuses solely on statistical co-occurrence patterns, this system will leverage LLMs for semantic understanding of product relationships, SLMs for efficient on-device processing, and Generative AI to create dynamic, personalised suggestions. Data from transactional records will be processed to uncover both explicit and latent associations between products, enabling cross-selling, upselling, and promotional strategies. The solution will include a recommendation engine capable of natural language explanations for its suggestions, improving transparency and user trust. Potential applications include e-commerce platforms, retail analytics, and personalised marketing campaigns. By combining the analytical power of MBA with the contextual reasoning capabilities of LLMs, the project seeks to deliver a next-generation intelligent recommendation system that improves customer engagement and increases business revenue.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Lawrence John O'Brien

Keywords

market basket analysis, large language models, small language models, generative ai, recommendation engine, contextual reasoning

Degrees

Bachelor of Science in Statistical Data Science