km1994/RES-Interview-Notes
该仓库主要记录 推荐系统 算法工程师相关的面试题
This resource provides comprehensive interview preparation materials for aspiring Recommendation System Algorithm Engineers. It offers detailed explanations and discussions on fundamental concepts, machine learning algorithms (like Collaborative Filtering, Matrix Factorization, Logistic Regression, FM, FFM, GBDT+LR), and deep learning models (such as AutoRec, NeuralCF, Deep Crossing, Wide&Deep, DeepFM). This helps candidates understand common interview questions and their answers, preparing them for technical discussions and problem-solving in recommendation system roles.
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Use this if you are a candidate interviewing for a Recommendation System Algorithm Engineer position and need to refresh your knowledge or prepare for technical questions across various recommendation system topics.
Not ideal if you are looking for an open-source library or tool to directly implement recommendation systems in a production environment.
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