Improving users' online shopping experience
Online product recommendation system
The Akaike Edge
Experienced ML and DL Ops teams
Efficient Deployment 0%
Ongoing Maintenance 0%
Selection of the appropriate variant for Collaborative Filtering approaches
Collaborative Filtering (CF) is one of the machine learning techniques for recommender systems to cluster similar users or items together and can be done in different ways. Multiple iterations were conducted to finalize the most appropriate variant of the Collaborative Filtering type recommendation for the task to showcase similar products for the selected product by the user.
The solution was deployed in a distributed cluster
Based on the data, the CF recommender system was built. Tables were created for a set of products that were purchased and viewed, and put together for the Collaborative-Filtering process. The solution was deployed in a distributed cluster of 100 machines, providing recommendations for more than 250,000 SKUs/products.
A/B testing and product validation
After analyzing the incoming traffic of real-time users, we were able to demonstrate to the customer that our recommender system improved the click as well as the conversion rates compared to displaying a random product to the visitors under a specific category. Upon proper validation, all the online traffic was exposed to the CF recommender system, improving the overall conversion rate for the customer.
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