2 个仓库
Awesome GitHub RepositoriesPreference Modeling
The process of creating mathematical models to represent and predict a user's latent interests.
Distinct from User Preference Settings: Focuses on the algorithmic modeling of interests rather than the UI for managing user settings.
Explore 2 awesome GitHub repositories matching user interface & experience · Preference Modeling. Refine with filters or upvote what's useful.
Awesome Preference Modeling GitHub Repositories
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- datawhalechina/team-learning-rs
datawhalechina/team-learning-rs
7,180在 GitHub 上查看这是一个端到端的推荐流水线和框架，专为构建生成式推荐引擎而设计。它利用大语言模型和向量搜索来促进个性化物品发现，并通过分词和语义对齐生成物品推荐。 该系统集成了生成式推荐引擎、多目标排序系统和向量搜索检索流水线。这些组件允许使用推理框架对物品进行推理，并应用重排序算法来优化输出结果的多样性。 该框架涵盖了推荐系统开发的完整生命周期，包括候选检索优化、用户偏好建模，以及用于部署的离线-在线流水线拆分。它还结合了硬件感知序列建模，以在不同硬件规模下保持性能。
Predicts user interests using multi-objective modeling and feature crossing to rank candidates precisely.
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
Combines generalized matrix factorization and multi-layer perceptron approaches to learn user preference interactions.
Pythoncollaborative-filteringdeep-learningrecommender-system
