Combining Fashion and Machine Learning to Develop a Recommendation System That Suggests Products to Customers Based on Their Profile & Preferences

To maintain high-quality user experience, they wanted to leverage highly adaptable ML models that could generate compatible product recommendations for consumers in the absence of co-purchase data by leveraging fashion domain expertise, product images, and textual metadata.

Solution

Quantiphi built a custom recommendation engine that generates personalized fashion product suggestions by analyzing customer clickstream data, uploaded products, purchase history and browsing patterns. In turn, understanding customer style, preferences and wishlists; thus, going beyond analyzing consumption patterns. Using object detection and computer vision, image embedded metadata is collected which is then used to find cosine similarity for suggesting a similar product as well as providing “Complete the Look” recommendations of apparel that can be paired together.

Result

• Hyper-personalization through “Complete the Look” recommendations based on user’s universal shopping history and fine-grained preferences
• “Pair with” product suggestions promote cross-selling and boost user engagements