入门推荐系统必读

入门推荐系统必读

【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 (LR融合模型 Practical Lessons from Predicting Clicks on Ads at Facebook(Factorization Machines(Wide & Deep Learning for Recommender Systems(Deep Interest Network for Click-Through Rate Prediction(Amazon.com recommendations: item-to-item collaborative filtering(Item2vec: Neural Item Embedding for Collaborative Filtering(Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations(DNN Deep neural networks for YouTube recommendations(Multi-Interest Network with Dynamic Routing for Recommendation at Tmall(The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries(https://cs.cmu.edu/~jgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf)

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