Optimize Ecommerce Search with Fine-Tuning and Automated Query Analysis

Optimize Ecommerce Search with Fine-Tuning and Automated Query Analysis

Marqo is collaborating with BluelightAI to create an industry leading ecommerce solution that enhances embedding model fine-tuning by automating the performance analysis of queries to products on your website.

Marqtune (available on Marqo Cloud), is an embedding model training platform that enhances product search and recommendations, tailored specifically for your data. BlueLightAI illuminates patterns in how your queries are performing, and where to make targeted improvements. By combining these tools, ecommerce businesses can confidently fine-tune models to deliver more precise search results, ultimately enhancing customer experience and driving revenue.

Motivation

The motivation behind this approach is to help ecommerce teams maximize the impact of their search optimization efforts by focusing on high-value product categories rather than isolated, low-performing queries. Instead of spending time troubleshooting individual search issues, which can be narrow in scope and limited in effect, Marqtune and BluelightAI Cobalt allow teams to identify and target entire product categories that drive business results.

What to Expect

In this blog, we'll show you how you can obtain information like impact per query from fine-tuning, as measured by an industry standard performance metric like NDCG. This graph is valuable as it helps identify areas where fine-tuning improved performance and where further adjustments might be needed to address gaps.

On top of this, we'll also be demonstrating how you can generate group labels for your queries so that you don't have to analyze one query at a time.

In this example, we fine-tuned an `e5-base-v2` base model on a 100k subset of our `Marqo-GS-10M` dataset of 10 million products from Google Shopping. This model was trained for 14 training epochs.

Step 1. Fine-Tune with Marqtune

The first step is to fine-tune your embedding model with Marqtune, available in Marqo Cloud.

Step 2. Collect Per Sample Performance Rate

For all of the models we want to compare against our newly fine-tuned model, we should collect the per query performance using the same queries across your models.

Step 3. Obtain Performance of Groups of Queries

Cobalt from BluelightAI generates group labels for your queries using advanced natural language clustering.

Step 4. Further Improvement of Your Model

With continued fine-tuning, you can refine your model and achieve even greater performance gains.

Conclusion

By fine-tuning with Marqtune and validating with BluelightAI, you're taking a proactive approach to improving and validating your models.