A unified, comprehensive and efficient recommendation library
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Updated
May 30, 2024 - Python
A unified, comprehensive and efficient recommendation library
Best Practices on Recommendation Systems
基于RFM和决策树模型构建专家推荐系统。融合了RFM模型和决策树模型,结合专业运营人员的业务经营,发掘潜在用户,进行推荐营销召回。
A Python implementation of LightFM, a hybrid recommendation algorithm.
A Python scikit for building and analyzing recommender systems
TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
Board game recommendation service
Sequence-to-Sequence Generative Model for Sequential Recommender System
The Interoperable Recommender is a data-driven solution aimed at enabling the participation of consumers in enhancing the resilience of the European energy infrastructure.
Board game recommendation engine
easy use user based collaborative filtering recommender system
Music recommendation system for providing a group of people with fresh music that fits all.
A set of session-based recommenders
A reimplementation of Paper NISER: Normalized Item and Session Representations to Handle Popularity Bias.
A general purpose recommender metrics library for fair evaluation.
Tiktok is an advanced multimedia recommender system that fuses the generative modality-aware collaborative self-augmentation and contrastive cross-modality dependency encoding to achieve superior performance compared to existing state-of-the-art multi-model recommenders.
Build a similarity-based image recommendation system for e-commerce that takes into account the visual similarity of items as an input for making product recommendations.
This project is a Spotify song recommender built with Streamlit. Given a user-selected song, the recommender suggests a song based on its cluster.
Build a wide-and-deep recommender with collaborative filters that takes advantage of patterns of repeat purchases to suggest both previously purchased and related products.
Add a description, image, and links to the recommender topic page so that developers can more easily learn about it.
To associate your repository with the recommender topic, visit your repo's landing page and select "manage topics."