Recoreco computes highly associated pairs of items (in the sense of 'people who are interested in X are also interested in Y') from interactions between users and items. It is a command line tool that expects a CSV file as input, where each line denotes an interaction between a user and an item and consists of a user identifier and an item identifier separated by a tab character. Recoreco by default outputs 10 associated items per item (with no particular ranking) in JSON format.
If you would like to learn more about the math behind the approach that recoreco is built on, checkout the book on practical machine learning: innovations in recommendation and the talk on real-time puppies and ponies from my friend Ted Dunning.
Helper methods for dealing with interaction data
Mapping between original string identifiers and internal indexes
Type definitions to map from tensors to Rust collections
Compute item indicators from a stream of interactions.