2 个仓库
Awesome GitHub RepositoriesRecommendation Benchmarks
Standardized environments and datasets used to compare the performance of recommendation algorithms.
Distinguishing note: Focuses on the environment for comparison rather than just the dataset or the model.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Recommendation Benchmarks. Refine with filters or upvote what's useful.
Awesome Recommendation Benchmarks GitHub Repositories
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- rucaibox/recbole
RUCAIBox/RecBole
4,487在 GitHub 上查看RecBole 是一个基于 PyTorch 的推荐框架，旨在构建、训练和评估各种推荐算法。它作为一个标准化的基准环境，允许使用公共数据集和一致的评估指标来比较不同的模型架构。 该项目为序列推荐和知识图谱集成提供了专门的工具包，能够根据用户历史预测项目序列或结合结构化的外部知识。它包括一个专用的超参数优化引擎，利用网格搜索和贝叶斯优化来调整模型配置。 该框架涵盖了广泛的功能，包括用于标准化交互日志的数据管理、具有分布式梯度同步和混合精度执行的训练管道，以及用于候选排序和多样性分析的综合评估工具。它支持多种推荐类型，例如通用协同过滤和点击率预测。 该库使用 Python 实现，并利用 PyTorch 作为其底层推荐框架。
Offers a standardized environment for comparing recommendation models using public datasets and consistent evaluation metrics.
Python - hexiangnan/neural_collaborative_filtering
hexiangnan/neural_collaborative_filtering
1,885在 GitHub 上查看Neural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit feedback by combining generalized matrix factorization and multi-layer perceptron networks through a shared final embedding layer. It captures both linear and non-linear interactions to model user preferences from historical data. The framework executes training and evaluation runs through a configuration-driven pipeline accessible via command-line interfaces, parsing hyperparameters such as learning rates, batch sizes, and latent dimensions. It optimizes implicit feedback mod
Measures the ranking quality of item recommendations using benchmark datasets with positive interactions and negative samples.
Pythoncollaborative-filteringdeep-learningrecommender-system
