Do Large Language Models make accurate personalized recommendations?

Kumo is an AI for quickly building highly accurate predictive models using the entirety of the data warehouse: multiple tables, both structured and unstructured. Kumo uses embedding tuning to uniquely combine pre-trained large language models (LLMs) with graph transformer architectures to improve accuracy by double-digits in days. Kumo’s models can be refined by domain experts for maximum performance improvements.

Recently the data science and engineering teams at Kumo AI ran several experiments with a product recommendation task to see how different LLMs improve recommendation accuracy. The task is to predict the products a customer is going to buy in the next 7 days. 

For the AI model to produce accurate recommendations it needs a detailed understanding of product properties as well as customer preferences. Products as well as customers are often associated with unstructured textual information like product names, product descriptions, customer reviews, and more. It is critical for the recommender system to include text understanding capability because so much information is stored in unstructured text. At the same time, graph information is highly valuable in recommender systems because it captures complex relationships between customers, products, and their interactions. Graphs represent the relationships between customers and products as edges, allowing the system to consider not just direct interactions (like purchases or ratings) but also indirect connections (e.g., customers who like similar products or products that are liked by similar customers). 

Furthermore, in some recommender systems, social relationships between customers (e.g., friendships) are also represented as graphs, which can enhance recommendations by incorporating preferences from a customer’s social circle. In graph-based systems, higher-order connections (i.e., multi-hop relationships) can be leveraged. 

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