信息推荐,是指根据用户的习惯、偏好或兴趣,从不断到来的大规模信息中识别满足用户兴趣的信息的过程。信息推荐任务中的信息往往称为物品(Item)。根据具体应用背景的不同,这些物品可以是新闻、电影、音乐、广告、商品等各种对象。俗称推荐系统。

信息推荐 (推荐系统,Recommendation System) 荟萃

入门学习

  1. 探索推荐引擎内部的秘密,第 1 部分 推荐引擎初探 IBM developerWorks
  2. 探索推荐引擎内部的秘密,第 2 部分 深入推荐引擎相关算法 - 协同过滤
  3. 探索推荐引擎内部的秘密,第 3 部分 深入推荐引擎相关算法 - 聚类
  4. 项亮《推荐系统实践》笔记(1,2)
  5. 推荐算法综述(一,二,三,四,五)
  6. 推荐系统,第一部分 方法和算法简介 第 2 部分 开源引擎简介
  7. 深度学习在推荐系统中的一些应用
  8. 《纽约时报》如何打造新一代推荐系统
  9. 深度学习在推荐算法上的应用进展
  10. 如何学习推荐系统? by 知乎
  11. 了解关于系统推荐算法的知识,有什么好的资源推荐? by 知乎
  12. 项亮推荐系统博士论文.pdf

  13. 微信公众号:resyschina 中国最专业的个性化推荐技术与产品社区。

  14. 饿了么推荐系统:从0到1

  15. 【直播回顾】21天搭建推荐系统:实现“千人千面”个性化推荐(含视频)

进阶文章

1997

  1. Recommender system (1997), P Resnick, HR Varian.

1998

  1. 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]
  2. 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

  1. A bayesian model for collaborative filtering (1999),Chien Y H, George E I.
    [http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/Bcollab.pdf]
  2. 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

  1. 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

  1. 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

  1. 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

  1. A maximum entropy approach for collaborative filtering (2004), Browning J, Miller D J.
    [http://www.yaroslavvb.com/papers/browning-maximum.pdf]
  2. 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

  1. 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]
  2. SVD-based collaborative filtering with privacy (2005), Polat H, Du W.
    [http://www.cis.syr.edu/~wedu/Research/paper/sac2004.pdf]

2007

  1. Improving regularized singular value decomposition for collaborative filtering (2007), A Paterek.
    [http://www.mimuw.edu.pl/~paterek/ap_kdd.pdf]
  2. 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]
  3. Restricted Boltzmann Machines for Collaborative Filtering (2007),R Salakhutdinov, A Mnih, G Hinton. [http://machinelearning.wustl.edu/mlpapers/paper_files/icml2007_SalakhutdinovMH07.pdf]

2008

  1. Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo (2008),R Salakhutdinov, et al.
    [http://www.cs.utoronto.ca/~amnih/papers/bpmf.pdf]
  2. 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

  1. 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]
  2. Bayesian Personalized Ranking from Implicit Feedback (2009), S Rendle, C Freudenthaler, Z Gantner.
    [https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf]
  3. 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]
  4. 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

  1. 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]
  2. 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]
  3. 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]
  4. 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

  1. 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]
  2. 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

  1. 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]
  2. 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]
  3. 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]
  4. Factorization Machines with libFM (2012),S Rendle.
    [http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf]
  5. 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]
  6. 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

  1. 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]
  2. Deep content-based music recommendation (2013), A Van den Oord, S Dieleman.
    [http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf]
  3. 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

  1. 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]
  2. 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

  1. Simple and scalable response prediction for display advertising (2015),O Chapelle, E Manavoglu, R Rosales. [http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]
  2. AutoRec- Autoencoders Meet Collaborative Filtering (2015), Suvash Sedhain, Aditya Krishna Menon, et al.
    [http://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf]
  3. Collaborative Deep Learning for Recommender Systems (2015), Hao Wang, N Wang, Dityan Yeung.
    [http://www.wanghao.in/mis/CDL.pdf]

2016

  1. 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]
  2. 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]
  3. 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]
  4. F2M Scalable Field-Aware Factorization Machines (2016),C Ma, Y Liao, Y Wang, Z Xiao. [https://pdfs.semanticscholar.org/bb29/9887ba700300757de7560dc34b48b127cdca.pdf]
  5. Blondel M, Fujino A, Ueda N, et al. Higher-order factorization machines[C]//Advances in Neural Information Processing Systems. 2016: 3351-3359. [http://papers.nips.cc/paper/6144-higher-order-factorization-machines.pdf]
  6. Shan Y, Hoens T R, Jiao J, et al. Deep Crossing: Web-scale modeling without manually crafted combinatorial features[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 255-262.
    [www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf]
  7. 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.
    [https://arxiv.org/pdf/1609.06018.pdf]
  8. Hybrid Collaborative Filtering with Autoencoders (2016), F Strub, J Mary, R Gaudel.
    [https://arxiv.org/pdf/1603.00806)]
  9. Wide & Deep Learning for Recommender Systems (2016),HT Cheng, L Koc, J Harmsen, T Shaked.
    [https://arxiv.org/pdf/1606.07792)]
  10. 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

  1. He X, Chua T S. Neural Factorization Machines for Sparse Predictive Analytics[J]. 2017:355-364.
    [https://arxiv.org/pdf/1708.05027.pdf]
  2. Ning Y, Shi Y, Hong L, et al. A Gradient-based Adaptive Learning Framework for E icient Personal Recommendation[J]. 2017. [http://people.cs.vt.edu/naren/papers/recs254-ningA.pdf] ​
  3. 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]
  4. 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]
  5. 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]
  6. 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)]
  7. Collaborative Deep Embedding via Dual Networks (2017), Yilei Xiong, Dahua Lin, et al.
    [https://openreview.net/pdf?id=r1w7Jdqxl)]
  8. Recurrent Recommender Networks (2017), Chao-Yuan Wu.
    [http://delivery.acm.org/10.1145/3020000/3018689/p495-wu.pdf?ip=221.226.125.130&id=3018689&acc=OA&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E5945DC2EABF3343C&CFID=995126498&CFTOKEN=96329132&__acm__=1508034746_8da751768f4ee19af912968914bbbaa6_)]

综述

  1. 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]
  2. 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
  3. Recommender systems: an introduction (2011), Zanker M, Felfernig A, Friedrich G. http://recommenderbook.net/media/szeged.pdf

Tutorial

  1. Tutorial: Recommender Systems IJCAI 2013 [http://ijcai13.org/files/tutorial_slides/td3.pdf\]
  2. Tutorial: Context In Recommender Systems 2016 [https://www.slideshare.net/irecsys/tutorial-context-in-recommender-systems]
  3. 融合用户上下文的个性化推荐 张敏, 清华大学 [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]
  4. 今日头条的人工智能技术实践 曹欢欢博士 [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]
  5. 基于循环神经网络的序列推荐 吴书 [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]
  6. 冷启动推荐的思考与进展 赵鑫 [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]
  7. Recommender Systems: A Brief Introduction 中科大 刘淇 [http://home.ustc.edu.cn/~zengxy/dm/courseware/A%20brief%20introduction%20to%20RecSys.pdf\]

视频教程

  1. 如何设计一个推荐系统 [https://www.youtube.com/watch?v=MZkxusQ6GNo]
  2. Recommender Systems | Coursera [https://www.coursera.org/specializations/recomender-systems]

代码

  1. annoy - Approximate Nearest Neighbors in Python optimized for memory usage. [https://github.com/spotify/annoy]
  2. fastFM - A library for Factorization Machines. [https://github.com/ibayer/fastFM]
  3. implicit - A fast Python implementation of collaborative filtering for implicit datasets. [https://github.com/benfred/implicit]
  4. libffm- A library for Field-aware Factorization Machine (FFM). [https://github.com/guestwalk/libffm]
  5. LightFM - A Python implementation of a number of popular recommendation algorithms. [https://github.com/lyst/lightfm]
  6. surprise - A scikit for building and analyzing recommender systems. [http://surpriselib.com]
  7. Crab-  a python recommender based on the popular packages NumPy, SciPy, matplotlib. The main repository seems to be abandoned. [http://muricoca.github.io/crab/]

领域专家

  1. 陈恩红 教授 从中国科技术大学教授,多媒体计算与通信教育部-微软重点实验室副主任。机器学习与数据挖掘、网络信息处理领的专家,相关研究获得国家杰出青年科学基金、教育部新世纪优秀人才计划等资助。曾担任KD、AAAI2012、ICDM、PAKDD、SDM3等30余个国际学术会议的程序委员。CCF理事、人工智能与模式识别专委会委员、数据库专委会委员、大数据专家委员会委员,中国人工智能学会理事,知识工程与分布智能专业委员会副主任委员、IEEE高级会员。 [http://staff.ustc.edu.cn/~cheneh/\]
  2. 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/]
  3. 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/~
  4. 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/]

  5. 谷文栋 微信公众号 resyschina , ResysChina发起人

  6. 洪亮劼 Etsy数据科学主管,前雅虎研究院高级研发经理


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