本文主要对推荐系统算法模型进行全面梳理，并整理成四张卡片，非常适合快速复习回顾。推荐系统概况：传统CTR、深度学习CTR、Graph Embedding、多任务学习。对比内容包括：算法思想、优缺点、网络结构、公式、代码实现等方面。涵盖：协同过滤、矩阵分解、LR、FM、FFM、GBDT+LR、XGBoost+LR
涵盖：NeuralCF、Deep Crossing、FNN、PNN、Wide&Deep、Deep&Cross、DeepFM、AFM、DIN、DIEN、DLRM
涵盖：Word2Vec、Item2Vec、DeepWalk、Node2Vec、EGES
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