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.
Varargs in Rel
We are excited to announce the support of varargs in Rel. You can use varargs to write more general code that works for multiple arities. Varargs can be useful when writing generic relations for common utilities.
Value Types in Rel
Value types help distinguish between different types of values, even though the underlying representation may be identical. Value types can be used to define other value types.
Asynchronous Transactions: Start Now, Check Later
RelationalAI's full suite of SDKs provides access to API endpoints which allow you to track long-running transactions in our Relational Knowledge Graph Management System (RKGMS). This is more reliable for long-running transactions, allows transactions to be canceled, and keeps a log of transactions that you can inspect either while they're running or at a later time.