AI Matching Engine for Luxury Marketplace
Budget: £250 – £750 GBP
I’m building an online multi-vendor platform dedicated to luxury goods and I want an AI-driven engine that automatically matches the right buyers with the right sellers. The core task is straightforward: take the data we already collect—product catalogue, buyer profiles, live browsing behaviour—and turn it into a machine-learning model that surfaces the best seller-buyer pairings in real time.
Key matching signals I care about are: price range, product type, seller ratings, buyer location, emerging trends, and individual client behaviour. The model should weigh these factors intelligently so that a buyer browsing vintage watches in Paris, for example, instantly sees the most reputable sellers in that niche, while a handbag collector in Tokyo is guided toward pieces aligned with her price ceiling and style history.
I’m committed to a machine-learning approach rather than a rules-only system, so you’re free to choose the specific algorithms—collaborative filtering, gradient boosting, deep learning, or a hybrid stack—as long as the outcome is explainable and improves over time with new data.
Deliverables I expect:
• A trained and documented ML model ready for deployment (Python preferred)
• Clear API endpoints or modules my dev team can plug into our existing marketplace stack (Laravel + Vue)
• A concise technical note describing feature engineering and tuning decisions, plus metrics that prove improved match relevance over a baseline heuristic
If you have experience with recommender systems, marketplace data, and luxury-segment nuances, let’s talk.
Key matching signals I care about are: price range, product type, seller ratings, buyer location, emerging trends, and individual client behaviour. The model should weigh these factors intelligently so that a buyer browsing vintage watches in Paris, for example, instantly sees the most reputable sellers in that niche, while a handbag collector in Tokyo is guided toward pieces aligned with her price ceiling and style history.
I’m committed to a machine-learning approach rather than a rules-only system, so you’re free to choose the specific algorithms—collaborative filtering, gradient boosting, deep learning, or a hybrid stack—as long as the outcome is explainable and improves over time with new data.
Deliverables I expect:
• A trained and documented ML model ready for deployment (Python preferred)
• Clear API endpoints or modules my dev team can plug into our existing marketplace stack (Laravel + Vue)
• A concise technical note describing feature engineering and tuning decisions, plus metrics that prove improved match relevance over a baseline heuristic
If you have experience with recommender systems, marketplace data, and luxury-segment nuances, let’s talk.
Related categories:
Python
Machine Learning (ML)
Laravel
Data Science
Vue.js
Data Analysis
Deep Learning
API Development