Problem

Amazon, being the largest online retailer in the world, has a vast catalog of products. It can be overwhelming for customers to navigate through this catalog and find products that they are interested in. This can lead to a poor user experience and lower sales for Amazon. To solve this problem, we needed to develop a product recommendation system that would help customers find products that they are more likely to buy.

We decided to use LLM (Latent Linear Model) as our recommendation algorithm because it is known for its ability to handle large datasets and provide accurate recommendations.

Learn more about LLM here.

Solution

We built an Amazon product recommendation system using LLM. The system takes into account the customer's purchase history, browsing history, and search queries to generate personalized recommendations. We also incorporated social data such as likes and shares on social media platforms into our algorithm.

The system was tested extensively with real users, and we made sure that the recommendations were relevant and accurate. We also added a feature where customers could provide feedback on the recommendations they received so that we could improve the accuracy of our algorithm over time.

Impact

Increased sales by X%
Improved user experience by providing personalized recommendations

"Quote of how incredible of an impact this had."

Team

Team Member #1 - Data Scientist
- Developed the LLM algorithm and implemented it in the system
- Analyzed data to improve the accuracy of recommendations
Team Member #2 - Software Engineer
- Built the recommendation system and integrated it with Amazon's existing infrastructure
- Worked on improving the user interface and incorporating customer feedback into the algorithm

Amazon LLM Boosts Sales with Personalized Recommendations