内容简介：推荐阅读
前言
1. 本次整理方式为时间线的形式，未来可能从其他视角和维度进行整理。
2. 由于本人知识有限，对于未出现在下文的推荐模型并不代表不经典，欢迎大家补充。
3. 文中提及的模型都标有对应参考文献，具体细节可阅读论文原文。
4. 整理此文的目的是给大家一个清晰的脉络，可当作一篇小小综述。从信息过载概念的提出到推荐系统的起源，从前深度学习时代的推荐系统到劲头正热的深度推荐系统，再到最后对于深度学习技术带来的推荐系统性能提升的质疑，每个阶段都是必不可少的。
5. 希望推荐系统领域未来仍然可以在曲折中前进。
6. 提前祝大家端午节快乐。
结尾
1. 回看推荐发展的30年，尽管前进的路上有过批评、有过怀疑，但一直在进步。
2. 至于推荐系统未来的发展何去何从，需要我们每个当前参与的人一起努力。
3. 也希望后人再总结的时候发现推荐系统在大方向上一直在茁壮成长。
参考文献
[1] Gross et al. The Managing Organizations: The Administrative Struggle. 1964.
[2] Karlgren et al. An Algebra for Recommendations.1990.
[3] Goldberg et al. Using collaborative filtering to weave an information tapestry.1992.
[4] User-based CF of Netnews GroupLens: An Open Architecture for Collaborative Filtering of Netnews. 1994.
[5] Balabanovic et al. Fab: Content-based, collaborative recommendation.1997.
[6] Daniel et al. Learning Collaborative Information Filters. ICML,1998.
[7] Sarwar et al. Item-based collaborative filtering recommendation algorithms. WWW, 2001.
[8] Linden et al. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE INTERNET COMPUT, 2003.
[9] Lemire et al. Slope One Predictors for Online Rating-Based Collaborative Filtering, SDM, 2005 .
[10] Bell et al. The BellKor solution to the Netflix Prize.2007.
[11] Simon Funk. Netflix Update: Try This at Home:https://sifter.org/~simon/journal/20061211.html, 2006.
[12] Ruslan et al. Restricted Boltzmann Machines for Collaborative Filtering,ICML, 2007.
[13] Mnih et al. Probabilistic matrix factorization. NIPS, 2008.
[14] Ma et al. Sorec: social recommendation using probabilistic matrix factorization. CIKM, 2008.
[15] Rendle et al. BPR: Bayesian personalized ranking from implicit feedback. UAI, 2009.
[16] Jamali et al. Trustwalker: a random walk model for combining trust-based and item-based recommendation. KDD, 2009.
[17] Koren et al. Matrix factorization techniques for recommender systems. Computer, 2009.
[18] Rendle. Factorization machines. ICDM, 2010.
[19] Ning et al. SLIM: Sparse linear methods for top-n recommender systems. ICDM, 2011.
[20] Ma et al. Recommender systems with social regularization. WSDM, 2011.
[21] Huang et al. Learning deep structured semantic models for web search using clickthrough data. 2013.
[22] Zhao et al. Leveraging social connections to improve personalized ranking for collaborative filtering. CIKM, 2014.
[23] He et al. Practical lessons from predicting clicks on ads at facebook. 2014.
[24] Sedhain et al. Autorec: Autoencoders meet collaborative filtering. WWW, 2015.
[25] Guo et al. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings. AAAI, 2015.
[26] Barkan et al. Item2vec: neural item embedding for collaborative filtering. 2016.
[27] Covington et al. Deep Neural Networks for YouTube Recommendations. 2016.
[28] Cheng et al. Wide & Deep Learning for Recommender Systems. 2016.
[29] Shan et al. Deep crossing: Web-scale modeling without manually crafted combinatorial features. KDD, 2016.
[30] Yuchin et al. Field-aware Factorization Machines for CTR Prediction. RecSys, 2016.
[31] Zhang et al. Deep Learning over Multi-field Categorical Data. UCL, 2016.
[32] Yanru et al. Product-based Neural Networks for User Response Prediction. ICDM, 2016.
[33] Kim et al. Convolutional Matrix Factorization for Document Context-Aware Recommendation. RecSys, 2016.
[34] Hidasi et al. Session-based Recommendations with Recurrent Neural Networks. 2016.
[35] He et al. Neural collaborative filtering. WWW, 2017.
[36] Guo et al. Deepfm: A factorization-machine based neural network for ctr prediction. IJCAI, 2017.
[37] Hsieh et al. Collaborative metric learning. WWW, 2017.
[38] Gai et al. Learning Piece-wise Linear Models from Large Scale Data for Ad ClickPrediction, 2017.
[39]He et al. Neural Factorization Machines for Sparse Predictive Analytics. 2017.
[40] Xiao et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks. IJCAI, 2017.
[41] Hsieh et al. Collaborative metric learning. WWW, 2017.
[42] Lei et al. Joint Deep Modeling of Users and Items Using Reviews for Recommendation. WSDM, 2017.
[43] Ruoxi et al. Deep & Cross Network for Ad Click Predictions. ADKDD, 2017.
[44] Ying et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD, 2018.
[45] Lian et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. KDD, 2018.
[46] Chen et al. Sequential Recommendation with User Memory Networks. WSDM 2018.
[47] Hongwei et al. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. CIKM, 2018.
[48] Zhou et al. Deep interest network for click-through rate prediction. KDD, 2018.
[49] Zhou et al. Deep interest evolution network for click-through rate prediction. AAAI, 2019.
[50] Huafeng et al. Deep Generative Ranking for Personalized Recommendation. Recsys, 2019.
[51] Xiang et al. Neural Graph Collaborative Filtering. SIGIR, 2019.
[52] Zhi-Hong et al. DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System. AAAI, 2019.
[53] Wu et al. Session-based Recommendation with Graph Neural Networks. AAAI, 2019.
[54] Maurizio et al. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. RecSys, 2019.
[55] Rendle et al. Neural Collaborative Filtering vs. Matrix Factorization Revisited. arXiv, 2020.
[56] Noveen et al. How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements. SIGIR, 2020
以上文献均可在 https://github.com/hongleizhang/RSPapers 中找到。
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