Using Rel for Machine Learning Data Preprocessing
RelationalAI's declarative modeling language Rel can be a powerful tool for machine learning data preprocessing. It is concise, readable, and facilitates testing and debugging in development. Rel can significantly simplify your machine learning data pipeline.
RelationalAI at the Knowledge Graph Conference
We are delighted to be attending and sponsoring the Knowledge Graph Conference again this year. Last year was an incredible event and this year is lining up to be just as exciting! Our VP of Strategic Development, Aisha Quaintance, is chairing the first ever Semantic Layer track. We are also presenting a masterclass and hands-on workshop, taking part in a fireside chat with Snowflake discussing financial services, and co-hosting a happy hour with Women in Data.
Hybrid and Content-based Recommender Systems in Rel - Part 3
In this series of blog posts, we show how to implement neighborhood-based, graph-based, content-based, and hybrid recommender systems using RelationalAI’s declarative modeling language, Rel. The implementations of these algorithms demonstrate the efficiency of our Relational Knowledge Graph System (RKGS) for aggregating over paths on large and sparse graphs, computing similarities using our graph analytics library, and building compact, easy to read models, without needing to transfer data outside of our system.
Graph-based Recommender Systems in Rel - Part 2
In our previous blog post, we explained how to model traditional neighborhood-based recommender systems in Rel. In what follows, we focus on modeling graph-based recommender systems.
Worksheets in the RAI Console
We are excited to announce worksheets, a new interface for submitting Rel queries. Worksheets allow you to develop blocks of Rel code and run them against a database. They can be shared with other users using their URLs.
Neighborhood-based Recommender Systems in Rel - Part 1
Recommender systems are one of the most successful and widely used applications of machine learning. Their use cases span a range of industry sectors such as e-commerce, entertainment, and social media. In this post, we focus on a fundamental and effective classical approach to recommender systems, which is neighborhood-based.