Cornac
A comparative Python framework for building, evaluating, and deploying multimodal recommender systems with auxiliary data.
What is Cornac?
Cornac is a Python framework for building and evaluating multimodal recommender systems. It provides a unified platform to experiment with models that use auxiliary data like text and images, offering tools for fast prototyping, model comparison, and deployment. It solves the problem of fragmented tooling in recommender system research and development.
Researchers and data scientists working on recommender systems, particularly those focusing on multimodal approaches or needing to compare multiple algorithms efficiently.
Developers choose Cornac for its comprehensive model library, ease of experimentation, and strong focus on multimodal data. Its compatibility with TensorFlow and PyTorch, along with built-in evaluation tools and deployment features, makes it a versatile and practical choice for both research and applied projects.
Overview
A Comparative Framework for Multimodal Recommender Systems
Use Cases
Best For
- Comparing the performance of different recommendation algorithms on a custom dataset.
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