Hybrid Recommendation Systems

Case study

Pipeline Prediction

We built a data aggregation pipeline that brings in different sources of data into a cohesive view of metadata and time series events for sales pipelines. We developed three types of models, one tree based model, and two types neural network models (one being embeddings + multilayer perceptron, and the other being embeddings + LSTM).

Case study

Recommendation Engine

Built a hybrid recommendation system that had a two embedding matching algorithm. It use data on users and properties to make more accurate recommendations.

Case study

eCommerce Recommendation Engine classifies apps on smart phone

Ecommerce Recommendation System

‍Built a classification and recommendation engine based on a vector clustering system.

By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.

Deny Accept

Contact Us

Hybrid Recommendation Systems