Development
R&D implied a massive historical data analysis which later directed us to the hypothesis, trial & error circle until a fair solution was found. At last, we ended up building an algorithm which uses neural networks and users' navigation data in order to facilitate the handmade categorization. We also built a set of algorithms using deep learning and NLP tools in order to automatically suggest missing categories. Some of the tools used where Gensim, Keras and NLTK.
Added to this, as human validation is part of the solution, we built an ultra intuitive and intelligent frontend using React + Redux, ES6 and Webpack to communicate with the built API.
Through ongoing development, we achieved results on a sprint basis. The most relevant result is that categorization has become much more efficient and we implemented some automation of category creation and product classification.
Read more about Mercadolibre on Wall Street Journal, Yahoo Finance & New York Times.
Tryolabs has been a key piece in optimising and extending our category trees at a much higher pace than that we could achieve with our former tools and teams. Through the mastering and customization of state of the art Machine Learning techniques, we've been able to increase item "findability" and conversion rates. We are so glad to have them collaborating with us in the quest for the best ecommerce solution, I would absolutely recommend them.