Building Real-time Product Recommendations with Generative AI

Implementing artificial intelligence (AI) and machine learning (ML) solutions in retail involves overcoming challenges such as updating legacy systems and eliminating slow batch processes to enhance customer experiences. A hypothetical scenario at a big-box retailer illustrates this journey, focusing on improving product recommendation systems plagued by outdated and inefficient batch processing. The existing system, reliant on overnight batch jobs using Apache Cassandra, struggles with latency and data staleness, impacting the retailer's ability to provide timely recommendations. To address these issues, the proposed solution involves leveraging generative AI to create vector embeddings for product data, enabling real-time recommendations through approximate nearest neighbor operations. This approach not only modernizes the recommendation process but also aligns with the retailer's marketing initiatives by keeping data up-to-date, ultimately paving the way for phasing out the legacy system. By utilizing open-source models and Cassandra's vector capabilities, the solution demonstrates a path to reducing technical debt and enhancing the retailer's digital infrastructure.

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