PreferredAI/cornac

Health v2 · maintenance only

Adoption not part of the score

1053 stars · 171 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Cornac is a Python framework for building and comparing multimodal recommender systems, with convenient support for auxiliary data such as item text, images, and social networks. It enables fast experiments and straightforward implementation of new recommendation models, integrating with TensorFlow and PyTorch.

Use cases

• compare recommender system algorithms on benchmark datasets
• build a collaborative filtering recommender with matrix factorization
• train a recommender using item text and image side information
• evaluate and reproduce recommendation algorithm results
• implement a new recommendation model for research
• benchmark BPR and other RecSys baselines

When to choose

• you need a well-regarded framework for reproducible recommender experiments
• your models leverage auxiliary multimodal data like text, images, or social graphs
• you want easy integration with TensorFlow or PyTorch
• you need trustworthy baseline implementations for RecSys comparison

When to avoid

• you need production recommendation serving rather than experimentation
• your use case is general machine learning outside recommendation
• you require a non-Python stack

Facets

library · maturity active

machine-learning benchmarking data-science machine-learning data-science python cross-platform recommender-systems collaborative-filtering matrix-factorization multimodal-learning recommendation-algorithms evaluation-framework algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="PreferredAI/cornac")