Deep Collaborative Filtering
We use deep collaborative filtering to understand people's preferences from cross-domain data sets. Unlike Matrix Factorization, our algorithms do not reduce an individual's preferences to a simplistic linear model but focus instead on all the complex patterns driving their tastes.
Semantic Graph Embedding
With graph embeddings we make sense of metadata, labels, tags, genres, actors and any ontological relation. Those approaches allow to find hidden correlations between seemingly unrelated or unpopular items.
Deep Content Extraction
We extract relevant information from text or image data using natural language processing and convolutional neural networks. Using this technology we can recommend an item that no one has rated yet by using its movie poster, synopsis or review.
Our algorithm uniquely learns the tastes and preferences of each individual while tackling the cold-start problem, that is, a recommendation of a new or rare item based on its content. Our backend uses a custom infrastructure built from scratch on Kubernetes which allows to re-train a user's profile in real time everytime they rate an item.
We are taking privacy and security seriously, and our incentives are fully aligned with our users. We keep our products ad free and unbiased in order to provide the most personal recommendations. In an effort to ensure that our products and algorithms remains efficient, biased free and secure, they do not retain any data but simply learn from choices to advance recommendation.
We are offering an individually tailored to businesses API service in order to solve the following problematics:
We're looking for innovative people to join our team.Work with us