Recommender system (1997), P Resnick, HR Varian.
[http://michael.hahsler.net/research/Recommender_SMU2011/EMIS_DSS_2012/Recomm.pdf]
1998
Empirical analysis of predictive algorithms for collaborative filtering (1998), John S Breese, David Heckerman, Carl M Kadie.
[http://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-12.pdf]
Clustering methods for collaborative filtering (1998), Ungar, L. H., D. P. Foster.
[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.44.7783&rep=rep1&type=pdf]
1999
A bayesian model for collaborative filtering (1999),Chien Y H, George E I.
[http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/Bcollab.pdf]
Using probabilistic relational models for collaborative filtering (1999), Lise Getoor, Mehran Sahami [http://citeseerx.ist.psu.edu/viewdoc/downloadjsessionid=52BCC5212B0117CBB8BA48A1D8230E30?doi=10.1.1.40.4507&rep=rep1&type=pdf;]
2001
Item-based Collaborative Filtering Recommendation Algorithms (2001), Badrul M Sarwar, George Karypis, Joseph A Konstan, John Riedl. [http://www10.org/cdrom/papers/pdf/p519.pdf]
2002
Hybrid recommender systems: Survey and experiments (2002), Burke R. [https://www.researchgate.net/profile/Robin_Burke/publication/263377228_Hybrid_Recommender_Systems_Survey_and_Experiments/links/5464ddc20cf2f5eb17ff3149.pdf]
2003
Amazon Recommendations Item-to-Item Collaborative Filtering (2003), G Linden, B Smith, et al.
[http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf]
2004
A maximum entropy approach for collaborative filtering (2004), Browning J, Miller D J.
[http://www.yaroslavvb.com/papers/browning-maximum.pdf]
Supporting user query relaxation in a recommender system (2004),Mirzadeh N, Ricci F, Bansal M. [https://www.researchgate.net/profile/Francesco_Ricci5/publication/221017551_Supporting_User_Query_Relaxation_in_a_Recommender_System/links/0deec524dcde30df0d000000.pdf]
2005
Case-based recommender systems: a unifying view.Intelligent Techniques for Web Personalization (2005),Lorenzi F, Ricci F. [www.inf.unibz.it/~ricci//papers/LorenziRicciCameraReady.pdf]
SVD-based collaborative filtering with privacy (2005), Polat H, Du W.
[http://www.cis.syr.edu/~wedu/Research/paper/sac2004.pdf]
2007
Improving regularized singular value decomposition for collaborative filtering (2007), A Paterek.
[http://www.mimuw.edu.pl/~paterek/ap_kdd.pdf]
Predicting Clicks Estimating the click-through rate for new ads (2007),M Richardson, E Dominowska.
[http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf]
Restricted Boltzmann Machines for Collaborative Filtering (2007),R Salakhutdinov, A Mnih, G Hinton. [http://machinelearning.wustl.edu/mlpapers/paper_files/icml2007_SalakhutdinovMH07.pdf]
2008
Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo (2008),R Salakhutdinov, et al.
[http://www.cs.utoronto.ca/~amnih/papers/bpmf.pdf]
Factorization Meets the Neighborhood- a Multifaceted Collaborative Filtering Model (2008),Y Koren. [http://www.academia.edu/download/35945687/Factorization_meets_the_neighborhood_a_multifaceted_collaborative_filtering_model.pdf]
2009
Utility-based repair of inconsistent requirements (2009), Felfernig A, Mairitsch M, Mandl M, et al.
[http://link.springer.com/content/pdf/10.1007/978-3-642-02568-6_17.pdf]
Bayesian Personalized Ranking from Implicit Feedback (2009), S Rendle, C Freudenthaler, Z Gantner.
[https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf]
Fast computation of query relaxations for knowledge-based recommenders (2009),Jannach D.
[http://ls13-www.cs.tu-dortmund.de/homepage/publications/jannach/Journal_AICOM09.pdf]
A hybrid approach to item recommendation in folksonomies (2009), Wetzker R, Umbrath W, Said A.
[http://www.dai-labor.de/fileadmin/Files/Publikationen/Buchdatei/wetzker_folksonomyrecommendation_esair2009_final.pdf]
2010
Click-Through Rate Estimation for Rare Events in Online Advertising (2010),X Wang, W Li, Y Cui, R Zhang.
[http://www.cs.cmu.edu/~./xuerui/papers/ctr.pdf]
Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine (2010), T Graepel, JQ Candela.
[http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_GraepelCBH10.pdf]
Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recommendation[C]//Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010: 81-90.
[https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010-PITF.pdf]
Factor in the Neighbors- Scalable and Accurate Collaborative Filtering (2010), Y Koren.
[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.4158&rep=rep1&type=pdf]
2011
Tag-aware recommender systems: a state-of-the-art survey (2011), Zhang Z K, Zhou T, Zhang Y C.
[http://arxiv.org/pdf/1202.5820.pdf]
Feature-Based Matrix Factorization (2011), T Chen, Z Zheng, Q Lu, W Zhang, Y Yu.
[https://arxiv.org/pdf/1109.2271.pdf?ref=theredish.com/web)]
2012
A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012 (2012),KW Wu, CS Ferng, CH Ho, AC Liang, CH Huang. [http://ntur.lib.ntu.edu.tw/retrieve/188498/03.pdf]
Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation (2012), T Chen, L Tang, Q Liu, D Yang, S Xie, X Cao, C Wu.
[http://curtis.ml.cmu.edu/w/courses/images/4/4e/AdditiveForestChen.pdf]
Rendle, Steffen. "Factorization machines with libfm." ACM Transactions on Intelligent Systems and Technology (TIST) 3.3 (2012): 57. [http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf]
Factorization Machines with libFM (2012),S Rendle.
[http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf]
Rendle S. Factorization machines with libfm[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3(3): 57. [http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf]
Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction (2012), M Jahrer, A Toscher, JY Lee, J Deng.
[https://pdfs.semanticscholar.org/eeb9/34178ea9320c77852eb89633e14277da41d8.pdf]
2013
Van den Oord A, Dieleman S, Schrauwen B. Deep content-based music recommendation[C]//Advances in neural information processing systems. 2013: 2643-2651.
[http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf]
Deep content-based music recommendation (2013), A Van den Oord, S Dieleman.
[http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf]
A Hybrid Approach with Collaborative Filtering for Recommender Systems (2013), G Badaro, H Hajj, et al.
[http://staff.aub.edu.lb/~we07/Publications/A%20Hybrid%20Approach%20with%20Collaborative%20Filtering%20for%20Recommender%20Systems.pdf]
2014
Zhang T, Zhang T, Zhang T, et al. Gradient boosting factorization machines[C]// ACM Conference on Recommender Systems. ACM, 2014:265-272.
[http://pdfs.semanticscholar.org/cd57/9e1e9cc350c3f7746e6ae6911a97e21ba27c.pdf]
Practical Lessons from Predicting Clicks on Ads at Facebook(2014), X He, J Pan, O Jin, T Xu, B Liu, T Xu, Y Shi.
[http://quinonero.net/Publications/predicting-clicks-facebook.pdf]
2015
Simple and scalable response prediction for display advertising (2015),O Chapelle, E Manavoglu, R Rosales. [http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]
Collaborative Deep Learning for Recommender Systems (2015), Hao Wang, N Wang, Dityan Yeung.
[http://www.wanghao.in/mis/CDL.pdf]
2016
Juan Y, Zhuang Y, Chin W S, et al. Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 43-50.
[http://ntucsu.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf]
Zhang W, Du T, Wang J, et al. Deep Learning over Multi-field Categorical Data[C]. european conference on information retrieval, 2016: 45-57. [https://arxiv.org/abs/1601.02376]
Factorization Meets the Item Embedding- Regularizing Matrix Factorization with Item Co-occurrence (2016), D Liang, J Altosaar, L Charlin, DM Blei.
[https://pdfs.semanticscholar.org/f14f/c33e0a351dff4f4e02510276604a93d1b9fa.pdf]
F2M Scalable Field-Aware Factorization Machines (2016),C Ma, Y Liao, Y Wang, Z Xiao. [https://pdfs.semanticscholar.org/bb29/9887ba700300757de7560dc34b48b127cdca.pdf]
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Chen J, Sun B, Li H, et al. Deep ctr prediction in display advertising[C]//Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016: 811-820.
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Hybrid Collaborative Filtering with Autoencoders (2016), F Strub, J Mary, R Gaudel.
[https://arxiv.org/pdf/1603.00806)]
Wide & Deep Learning for Recommender Systems (2016),HT Cheng, L Koc, J Harmsen, T Shaked.
[https://arxiv.org/pdf/1606.07792)]
Deep Neural Networks for YouTube Recommendations (2016), Paul Covington, Jay Adams, Emre Sargin. [https://www.researchgate.net/publication/307573656_Deep_Neural_Networks_for_YouTube_Recommendations)]
2017
He X, Chua T S. Neural Factorization Machines for Sparse Predictive Analytics[J]. 2017:355-364.
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Qu Y, Cai H, Ren K, et al. Product-Based Neural Networks for User Response Prediction[C]// IEEE, International Conference on Data Mining. IEEE, 2017:1149-1154.
[https://arxiv.org/pdf/1611.00144.pdf]
Guo H, Tang R, Ye Y, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C]// Twenty-Sixth International Joint Conference on Artificial Intelligence. 2017:1725-1731.
[https://arxiv.org/pdf/1703.04247.pdf]
Xiao J, Ye H, He X, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks[J]. 2017. [https://ru.arxiv.org/pdf/1708.04617.pdf]
A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017),Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, Fangxi Zhang.
[http://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14676/13916)]
Collaborative Deep Embedding via Dual Networks (2017), Yilei Xiong, Dahua Lin, et al.
[https://openreview.net/pdf?id=r1w7Jdqxl)]
Deep Learning based Recommender System: A Survey and New Perspectives 用于推荐系统的所有深度学习方法 [https://arxiv.org/pdf/1707.07435.pdf]
Toward the next generation of recommender systems:A survey of the state-of-the-art and possiblie extensions (2005), Adomavicius G, Tuzhilin A.http://people.stern.nyu.edu/atuzhili/pdf/TKDE-Paper-as-Printed.pdf
Recommender systems: an introduction (2011), Zanker M, Felfernig A, Friedrich G.http://recommenderbook.net/media/szeged.pdf
Tutorial
Tutorial: Recommender Systems IJCAI 2013 [http://ijcai13.org/files/tutorial_slides/td3.pdf\]
Tutorial: Context In Recommender Systems 2016 [https://www.slideshare.net/irecsys/tutorial-context-in-recommender-systems]
Recommender Systems | Coursera [https://www.coursera.org/specializations/recomender-systems]
代码
annoy - Approximate Nearest Neighbors in Python optimized for memory usage. [https://github.com/spotify/annoy]
fastFM - A library for Factorization Machines. [https://github.com/ibayer/fastFM]
implicit - A fast Python implementation of collaborative filtering for implicit datasets. [https://github.com/benfred/implicit]
libffm- A library for Field-aware Factorization Machine (FFM). [https://github.com/guestwalk/libffm]
LightFM - A Python implementation of a number of popular recommendation algorithms. [https://github.com/lyst/lightfm]
surprise - A scikit for building and analyzing recommender systems. [http://surpriselib.com]
Crab- a python recommender based on the popular packages NumPy, SciPy, matplotlib. The main repository seems to be abandoned. [http://muricoca.github.io/crab/]
唐杰 博士 清华大学计算机系副教授、博士生导师。主要研究兴趣包括:社会网络分析、数据挖掘、机器学习和语义Web。研发了研究者社会网络ArnetMiner系统,吸引全球220个国家和地区432万独立IP的访问。荣获首届国家自然科学基金优秀青年基金,2012中国计算机学会青年科学家奖、2010年清华大学学术新人奖(清华大学40岁以下教师学术最高奖)、2011年北京市科技新星、IBM全球创新教师奖以及KDD’12 Best Poster Award、PKDD’11 Best Student Paper Runnerup和JCDL’12 Best Student Paper Nomination。 [http://keg.cs.tsinghua.edu.cn/jietang/]
张敏, 清华大学计算机科学与技术系副教授,博士生导师。主要研究领域为信息检索、个性化推荐、用户画像与建模、用户行为分析。现任智能技术与系统国家重点实验中心实验室科研副主任、网络与媒体技术教育部-微软重点实验室副主任。在重要的国际期刊和会议上发表多篇学术论文,包括JIR、IJCAI、SIGIR、WWW、CIKM、WSDM等,Google Scholar引用约2500次。已授权专利11项。担任重要国际期刊TOIS编委,国际会议WSDM 2017和AIRS2016程序委员会主席,SIGIR 2018 short paper主席, WWW,SIGIR,CIKM,WSDM等重要国际会议的领域主席或资深审稿人。现任中国中文信息学会理事,中国计算机学会高级会员。http://www.thuir.org/group/~mzhang/~
赵鑫,北京大学博士,中国人民大学信息学院教师。研究领域为社交数据挖掘和自然语言处理领域,共发表CCF A/B、SCI论文40余篇, Google Scholar引用1500余次。博士期间的研究工作主要集中在社交媒体用户话题兴趣建模研究,同时获得谷歌中国博士奖研金和微软学者称号。其中ECIR’11提出的Twitter-LDA成为短文本主题建模重要基准比较方法之一,单文引用次数近700次。目前主要关注与社会经济紧密相关的商业大数据挖掘,研究用户意图检测、用户画像以及推荐系统,将理论技术运用到实践之中,承担国家自然科学青年基金、北京市自然科学青年基金,入选第二届CCF“青年人才托举计划”。担任多个国际顶级期刊和学术会议评审、AIRS 2016出版主席、SMP 2017领域主席以及NLPCC 2017领域主席。 [http://playbigdata.com/batmanfly/]