推荐系统或信息发现领域，有哪些经典的论文?
最近正好整理了一下推荐系统入门必读的10篇经典论文，涵盖了排序，召回以及机制策略。 - 【排序】大规模稀疏线性排序模型FTRL工程实现 [Ad click prediction: a view from the trenches](http://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf) - 【排序】GBDT LR融合模型 [Practical Lessons from Predicting Clicks on Ads at Facebook](https://quinonero.net/Publications/predicting-clicks-facebook.pdf) - 【排序】因子分解机，召回和排序的利器，速度快效果好[Factorization Machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) - 【排序】深度学习模型Wide&Deep [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792) - 【排序】深度兴趣网络，捕获用户历史行为与候选物料相关性[Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978) - 【召回】基于内容的协同过滤及其变种可以说是召回中应用最广泛算法之一，这篇是最经典的ItemCF[Online Shopping for Electronics, Apparel, Computers, Books, DVDs & more recommendations: item-to-item collaborative filtering](Amazon.com recommendations: item-to-item collaborative filtering) - 【召回】无监督embedding学习，用于向量召回[Item2vec: Neural Item Embedding for Collaborative Filtering ](https://arxiv.org/pdf/1603.04259v2.pdf) - 【召回】双塔DNN [Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations](https://www.researchgate.net/publication/335768638_Sampling-bias-corrected_neural_modeling_for_large_corpus_item_recommendations) - 【召回】Youtube DNN[Deep neural networks for YouTube recommendations](https://www.researchgate.net/publication/307573656_Deep_Neural_Networks_for_YouTube_Recommendations) - 【召回】考虑用户多峰兴趣的深度召回模型[Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/pdf/1904.08030) - 【重排&机制策略】多样性重排MMR,这篇要告诉大家推荐系统中还需要很多机制策略[The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries](https://www.cs.cmu.edu/~jgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf) 为了方便大家学习，我把一些相关的经典文章和代码实现进行了打包汇总并放在了github仓库里，感兴趣的同学可以看看～  