ZiyaoGeng/RecLearn
A TensorFlow2 framework implementing neural recommender system models including DeepFM, DCN, and xDeepFM for CTR prediction and matching recommendation.
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RecLearn is a recommender learning framework for students and beginners that provides implementations of recommendation algorithms across two stages: matching recommendation (Top-k) and ranking recommendation (CTR prediction). The framework includes various neural network architectures implemented with TensorFlow2.x, including factorization machines, deep cross networks, and deep feature interaction models. It supports standard datasets like Criteo and provides example code for training and evaluation.
Frequently asked
- What is ZiyaoGeng/RecLearn?
- A TensorFlow2 framework implementing neural recommender system models including DeepFM, DCN, and xDeepFM for CTR prediction and matching recommendation.
- Is RecLearn open source?
- Yes — ZiyaoGeng/RecLearn is open source, released under the MIT license.
- What language is RecLearn written in?
- ZiyaoGeng/RecLearn is primarily written in Python.
- How popular is RecLearn?
- ZiyaoGeng/RecLearn has 2k stars on GitHub.
- Where can I find RecLearn?
- ZiyaoGeng/RecLearn is on GitHub at https://github.com/ZiyaoGeng/RecLearn.