westlake-repl/NineRec
Multimodal Dataset and Benchmark for Multi-domain and Cross-domain Recommendation System
This project provides a comprehensive collection of datasets for building and evaluating recommendation systems, especially those that need to learn from various types of information (like images and text) or adapt recommendations across different platforms or product categories. It offers diverse input data, including raw images, user interaction sequences, and item descriptions, to help you develop smarter recommendation engines. It's designed for data scientists, machine learning engineers, and researchers who build and refine recommendation algorithms for e-commerce, content platforms, or other personalized services.
103 stars. No commits in the last 6 months.
Use this if you are developing or evaluating recommendation systems that need to incorporate various data types (multimodal) or transfer learning capabilities across different domains or platforms.
Not ideal if you are looking for a simple, ID-based recommendation dataset without multimodal features or cross-domain transfer learning considerations.
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103
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Python
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Last pushed
Oct 06, 2024
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