GitHub - weiweifan/Big-Data-Resources: 大数据/数据挖掘/推荐系统/机器学习相关资源

Netflix 推荐系统：第一部分 http://blog.csdn.net/bornhe/article/details/8222450
Netflix 推荐系统：第二部分 http://blog.csdn.net/bornhe/article/details/8222497
探索推荐引擎内部的秘密 http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html
推荐系统resys小组线下活动见闻2009-08-22 http://www.tuicool.com/articles/vUvQVn
Recommendation Engines Seminar Paper, Thomas Hess, 2009: 推荐引擎的总结性文章 http://www.slideshare.net/antiraum/recommender-engines-seminar-paper
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, Adomavicius, G.; Tuzhilin, A., 2005 http://dl.acm.org/citation.cfm?id=1070751
A Taxonomy of RecommenderAgents on the Internet, Montaner, M.; Lopez, B.; de la Rosa, J. L., 2003 http://www.springerlink.com/index/KK844421T5466K35.pdf
A Course in Machine Learning http://ciml.info/
基于mahout构建社会化推荐引擎 http://www.doc88.com/p-745821989892.html
个性化推荐技术漫谈 http://blog.csdn.net/java060515/archive/2007/04/19/1570243.aspx
Design of Recommender System http://www.slideshare.net/rashmi/design-of-recommender-systems
How to build a recommender system http://www.slideshare.net/blueace/how-to-build-a-recommender-system-presentation
推荐系统架构小结 http://blog.csdn.net/idonot/article/details/7996733
System Architectures for Personalization and Recommendation http://techblog.netflix.com/2013/03/system-architectures-for.html
The Netflix Tech Blog http://techblog.netflix.com/
百分点推荐引擎——从需求到架构http://www.infoq.com/cn/articles/baifendian-recommendation-engine
推荐系统在InfoQ上的内容 http://www.infoq.com/cn/recommend
推荐系统实时化的实践和思考 http://www.infoq.com/cn/presentations/recommended-system-real-time-practice-thinking
质量保证的推荐实践 http://www.infoq.com/cn/news/2013/10/testing-practice/
推荐系统的工程挑战 http://www.infoq.com/cn/presentations/Recommend-system-engineering
社会化推荐在人人网的应用 http://www.infoq.com/cn/articles/zyy-social-recommendation-in-renren/
利用20%时间开发推荐引擎 http://www.infoq.com/cn/presentations/twenty-percent-time-to-develop-recommendation-engine
使用Hadoop和 Mahout实现推荐引擎 http://www.jdon.com/44747
SVD 简介 http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html
Netflix推荐系统：从评分预测到消费者法则 http://blog.csdn.net/lzt1983/article/details/7696578

《推荐系统实践》的Reference

  	http://en.wikipedia.org/wiki/Information_overload 
  　　 P1 
  　　 
  　　http://www.readwriteweb.com/archives/recommender_systems.php 
  　　(A Guide to Recommender System) P4 
  　　 
  　　 
  　　http://en.wikipedia.org/wiki/Cross-selling 
  　　 (Cross Selling) P6 
  　　 
  　　http://blog.kiwitobes.com/?p=58 ， http://stanford2009.wikispaces.com/ 
  　　(课程：Data Mining and E-Business: The Social Data Revolution) P7 
  　　 
  　　 http://thesearchstrategy.com/ebooks/an%20introduction%20to%20search%20engines%20and%20web%20navigation.pdf 
  　　（An Introduction to Search Engines and Web Navigation） p7 
  　　 
  　　http://www.netflixprize.com/ 
  　　p8 
  　　 
  　　http://cdn-0.nflximg.com/us/pdf/Consumer_Press_Kit.pdf 
  　　 p9 
  　　 
  　　 http://stuyresearch.googlecode.com/hg-history/c5aa9d65d48c787fd72dcd0ba3016938312102bd/blake/resources/p293-davidson.pdf 
  　　(The Youtube video recommendation system) p9 
  　　 
  　　 http://www.slideshare.net/plamere/music-recommendation-and-discovery 
  　　( PPT: Music Recommendation and Discovery) p12 
  　　 
  　　http://www.facebook.com/instantpersonalization/ 
  　　P13 
  　　 
  　　 http://about.digg.com/blog/digg-recommendation-engine-updates 
  　　 (Digg Recommendation Engine Updates) P16 
  　　 
  　　 http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36955.pdf 
  　　 (The Learning Behind Gmail Priority Inbox)p17 
  　　 
  　　http://www.grouplens.org/papers/pdf/mcnee-chi06-acc.pdf 
  　　(Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20 
  　　 
  　　http://www-users.cs.umn.edu/~mcnee/mcnee-cscw2006.pdf 
  　　 (Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23 
  　　 
  　　http://www.sigkdd.org/explorations/issues/9-2-2007-12/7-Netflix-2.pdf 
  　　 (Major componets of the gravity recommender system) P25 
  　　 
  　　http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext 
  　　(What is a Good Recomendation Algorithm?) P26 
  　　 
  　　http://research.microsoft.com/pubs/115396/evaluationmetrics.tr.pdf 
  　　 (Evaluation Recommendation Systems) P27 
  　　 
  　　http://mtg.upf.edu/static/media/PhD_ocelma.pdf 
  　　(Music Recommendation and Discovery in the Long Tail) P29 
  　　 
  　　http://ir.ii.uam.es/divers2011/ 
  　　(Internation Workshop on Novelty and Diversity in Recommender Systems) p29 
  　　 
  　　http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_21.pdf 
  　　(Auralist: Introducing Serendipity into Music Recommendation ) P30 
  　　 
  　　http://www.springerlink.com/content/978-3-540-78196-7/#section=239197&page=1&locus=21 
  　　(Metrics for evaluating the serendipity of recommendation lists) P30 
  　　 
  　　http://dare.uva.nl/document/131544 
  　　(The effects of transparency on trust in and acceptance of a content-based art recommender) P31 
  　　 
  　　http://brettb.net/project/papers/2007%20Trust-aware%20recommender%20systems.pdf 
  　　 (Trust-aware recommender systems) P31 
  　　 
  　　http://recsys.acm.org/2011/pdfs/RobustTutorial.pdf 
  　　(Tutorial on robutness of recommender system) P32 
  　　 
  　　http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html 
  　　 (Five Stars Dominate Ratings) P37 
  　　 
  　　http://www.informatik.uni-freiburg.de/~cziegler/BX/ 
  　　(Book-Crossing Dataset) P38 
  　　 
  　　http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html 
  　　(Lastfm Dataset) P39 
  　　 
  　　http://mmdays.com/2008/11/22/power_law_1/ 
  　　（浅谈网络世界的Power Law现象） P39 
  　　 
  　　http://www.grouplens.org/node/73/ 
  　　(MovieLens Dataset) P42 
  　　 
  　　http://research.microsoft.com/pubs/69656/tr-98-12.pdf 
  　　(Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49 
  　　 
  　　http://vimeo.com/1242909 
  　　(Digg Vedio) P50 
  　　 
  　　http://glaros.dtc.umn.edu/gkhome/fetch/papers/itemrsCIKM01.pdf 
  　　 (Evaluation of Item-Based Top-N Recommendation Algorithms) P58 
  　　 
  　　http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf 
  　　(Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59 
  　　 
  　　http://glinden.blogspot.com/2006/03/early-amazon-similarities.html 
  　　 (Greg Linden Blog) P63 
  　　 
  　　http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf 
  　　(One-Class Collaborative Filtering) P67 
  　　 
  　　http://en.wikipedia.org/wiki/Stochastic_gradient_descent 
  　　(Stochastic Gradient Descent) P68 
  　　 
  　　http://www.ideal.ece.utexas.edu/seminar/LatentFactorModels.pdf 
  　　 (Latent Factor Models for Web Recommender Systems) P70 
  　　 
  　　http://en.wikipedia.org/wiki/Bipartite_graph 
  　　(Bipatite Graph) P73 
  　　 
  　　http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4072747&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4072747 
  　　(Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74 
  　　 
  　　http://www-cs-students.stanford.edu/~taherh/papers/topic-sensitive-pagerank.pdf 
  　　(Topic Sensitive Pagerank) P74 
  　　 
  　　http://www.stanford.edu/dept/ICME/docs/thesis/Li-2009.pdf 
  　　(FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77 
  　　 
  　　https://www.aaai.org/ojs/index.php/aimagazine/article/view/1292 
  　　 (LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80
  　　 
  　　http://research.yahoo.com/files/wsdm266m-golbandi.pdf 
  　　( adaptive bootstrapping of recommender systems using decision trees) P87 
  　　 
  　　http://en.wikipedia.org/wiki/Vector_space_model 
  　　(Vector Space Model) P90 
  　　 
  　　http://tunedit.org/challenge/VLNetChallenge 
  　　(冷启动问题的比赛) P92 
  　　 
  　　http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf 
  　　 (Latent Dirichlet Allocation) P92 
  　　 
  　　http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence 
  　　 (Kullback–Leibler divergence) P93 
  　　 
  　　http://www.pandora.com/about/mgp 
  　　(About The Music Genome Project) P94 
  　　 
  　　http://en.wikipedia.org/wiki/List_of_Music_Genome_Project_attributes 
  　　(Pandora Music Genome Project Attributes) P94 
  　　 
  　　http://www.jinni.com/movie-genome.html 
  　　(Jinni Movie Genome) P94 
  　　 
  　　http://www.shilad.com/papers/tagsplanations_iui2009.pdf 
  　　 (Tagsplanations: Explaining Recommendations Using Tags) P96 
  　　 
  　　http://en.wikipedia.org/wiki/Tag_(metadata) 
  　　(Tag Wikipedia) P96 
  　　 
  　　http://www.shilad.com/shilads_thesis.pdf 
  　　(Nurturing Tagging Communities) P100 
  　　 
  　　http://www.stanford.edu/~morganya/research/chi2007-tagging.pdf 
  　　 (Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100 
  　　 
  　　http://www.google.com/url?sa=t&rct=j&q=delicious%20dataset%20dai-larbor&amp;source=web&cd=1&ved=0CFIQFjAA&url=http%3A%2F%2Fwww.dai-labor.de%2Fen%2Fcompetence_centers%2Firml%2Fdatasets%2F&ei=1R4JUKyFOKu0iQfKvazzCQ&;usg=AFQjCNGuVzzKIKi3K2YFybxrCNxbtKqS4A&amp;cad=rjt 
  　　(Delicious Dataset) P101 
  　　 
  　　http://research.microsoft.com/pubs/73692/yihgoca-www06.pdf 
  　　 (Finding Advertising Keywords on Web Pages) P118 
  　　 
  　　http://www.kde.cs.uni-kassel.de/ws/rsdc08/ 
  　　(基于标签的推荐系统比赛) P119 
  　　 
  　　http://delab.csd.auth.gr/papers/recsys.pdf 
  　　（Tag recommendations based on tensor dimensionality reduction）P119 
  　　 
  　　http://www.l3s.de/web/upload/documents/1/recSys09.pdf 
  　　(latent dirichlet allocation for tag recommendation) P119 
  　　 
  　　http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&rep=rep1&amp;type=pdf 
  　　(Folkrank: A ranking algorithm for folksonomies) P119 
  　　 
  　　http://www.grouplens.org/system/files/tagommenders_numbered.pdf 
  　　 (Tagommenders: Connecting Users to Items through Tags) P119 
  　　 
  　　http://www.grouplens.org/system/files/group07-sen.pdf 
  　　(The Quest for Quality Tags) P120 
  　　 
  　　http://2011.camrachallenge.com/ 
  　　(Challenge on Context-aware Movie Recommendation) P123 
  　　 
  　　http://bits.blogs.nytimes.com/2011/09/07/the-lifespan-of-a-link/ 
  　　(The Lifespan of a link) P125 
  　　 
  　　http://www0.cs.ucl.ac.uk/staff/l.capra/publications/lathia_sigir10.pdf 
  　　 (Temporal Diversity in Recommender Systems) P129 
  　　 
  　　http://staff.science.uva.nl/~kamps/ireval/papers/paper_14.pdf 
  　　 (Evaluating Collaborative Filtering Over Time) P129 
  　　 
  　　http://www.google.com/places/ 
  　　(Hotpot) P139 
  　　 
  　　http://www.readwriteweb.com/archives/google_launches_recommendation_engine_for_places.php 
  　　(Google Launches Hotpot, A Recommendation Engine for Places) P139 
  　　 
  　　http://xavier.amatriain.net/pubs/GeolocatedRecommendations.pdf 
  　　 (geolocated recommendations) P140 
  　　 
  　　http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html 
  　　(A Peek Into Netflix Queues) P141 
  　　 
  　　http://www.cs.umd.edu/users/meesh/420/neighbor.pdf 
  　　(Distance Browsing in Spatial Databases1) P142 
  　　 
  　　http://www.eng.auburn.edu/~weishinn/papers/MDM2010.pdf 
  　　 (Efﬁcient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143 
  　　 
  　　 
  　　http://blog.nielsen.com/nielsenwire/consumer/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most/ 
  　　(Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144 
  　　 
  　　http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36371.pdf 
  　　(Suggesting Friends Using the Implicit Social Graph) P145 
  　　 
  　　http://blog.nielsen.com/nielsenwire/online_mobile/friends-frenemies-why-we-add-and-remove-facebook-friends/ 
  　　(Friends & Frenemies: Why We Add and Remove Facebook Friends) P147 
  　　 
  　　http://snap.stanford.edu/data/ 
  　　(Stanford Large Network Dataset Collection) P149 
  　　 
  　　http://www.dai-labor.de/camra2010/ 
  　　(Workshop on Context-awareness in Retrieval and Recommendation) P151 
  　　 
  　　http://www.comp.hkbu.edu.hk/~lichen/download/p245-yuan.pdf 
  　　 (Factorization vs. Regularization: Fusing Heterogeneous 
  　　Social Relationships in Top-N Recommendation) P153 
  　　 
  　　http://www.infoq.com/news/2009/06/Twitter-Architecture/ 
  　　(Twitter, an Evolving Architecture) P154 
  　　 
  　　http://www.google.com/url?sa=t&rct=j&q=&esrc=s&amp;source=web&cd=2&ved=0CGQQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.165.3679%26rep%3Drep1%26type%3Dpdf&ei=dIIJUMzEE8WviQf5tNjcCQ&usg=AFQjCNGw2bHXJ6MdYpksL66bhUE8krS41w&sig2=5EcEDhRe9S5SQNNojWk7_Q 
  　　(Recommendations in taste related domains) P155 
  　　 
  　　http://www.ercim.eu/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf 
  　　(Comparing Recommendations Made by Online Systems and Friends) P155 
  　　 
  　　http://techcrunch.com/2010/04/22/facebook-edgerank/ 
  　　(EdgeRank: The Secret Sauce That Makes Facebook's News Feed Tick) P157 
  　　 
  　　http://www.grouplens.org/system/files/p217-chen.pdf 
  　　(Speak Little and Well: Recommending Conversations in Online Social Streams) P158 
  　　 
  　　http://blog.linkedin.com/2008/04/11/learn-more-abou-2/ 
  　　(Learn more about “People You May Know”) P160 
  　　 
  　　http://domino.watson.ibm.com/cambridge/research.nsf/58bac2a2a6b05a1285256b30005b3953/8186a48526821924852576b300537839/$FILE/TR%202009.09%20Make%20New%20Frends.pdf 
  　　(“Make New Friends, but Keep the Old” – Recommending People on Social Networking Sites) P164 
  　　 
  　　http://www.google.com.hk/url?sa=t&rct=j&q=social+recommendation+using+prob&source=web&amp;cd=2&ved=0CFcQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.141.465%26rep%3Drep1%26type%3Dpdf&amp;ei=LY0JUJ7OL9GPiAfe8ZzyCQ&usg=AFQjCNH-xTUWrs9hkxTA8si5fztAdDAEng 
  　　(SoRec: Social Recommendation Using Probabilistic Matrix) P165 
  　　 
  　　http://olivier.chapelle.cc/pub/DBN_www2009.pdf 
  　　(A Dynamic Bayesian Network Click Model for Web Search Ranking) P177 
  　　 
  　　http://www.google.com.hk/url?sa=t&rct=j&q=online+learning+from+click+data+spnsored+search&amp;source=web&cd=1&ved=0CFkQFjAA&amp;url=http%3A%2F%2Fwww.research.yahoo.net%2Ffiles%2Fp227-ciaramita.pdf&ei=HY8JUJW8CrGuiQfpx-XyCQ&usg=AFQjCNE_CYbEs8DVo84V-0VXs5FeqaJ5GQ&cad=rjt 
  　　(Online Learning from Click Data for Sponsored Search) P177 
  　　 
  　　http://www.cs.cmu.edu/~deepay/mywww/papers/www08-interaction.pdf 
  　　(Contextual Advertising by Combining Relevance with Click Feedback) P177 
  　　http://tech.hulu.com/blog/2011/09/19/recommendation-system/ 
  　　(Hulu 推荐系统架构) P178 
  　　 
  　　http://mymediaproject.codeplex.com/ 
  　　(MyMedia Project) P178 
  　　 
  　　http://www.grouplens.org/papers/pdf/www10_sarwar.pdf 
  　　(item-based collaborative filtering recommendation algorithms) P185 
  　　 
  　　http://www.stanford.edu/~koutrika/Readings/res/Default/billsus98learning.pdf 
  　　(Learning Collaborative Information Filters) P186 
  　　 
  　　http://sifter.org/~simon/journal/20061211.html 
  　　(Simon Funk Blog:Funk SVD) P187 
  　　 
  　　http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf 
  　　(Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190 
  　　 
  　　http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh26.pdf 
  　　(Time-dependent Models in Collaborative Filtering based Recommender System) P193 
  　　 
  　　http://sydney.edu.au/engineering/it/~josiah/lemma/kdd-fp074-koren.pdf 
  　　(Collaborative filtering with temporal dynamics) P193 
  　　 
  　　http://en.wikipedia.org/wiki/Least_squares 
  　　(Least Squares Wikipedia) P195 
  　　 
  　　http://www.mimuw.edu.pl/~paterek/ap_kdd.pdf 
  　　(Improving regularized singular value decomposition for collaborative filtering) P195 
  　　 
  　　http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf 
  　　 (Factorization Meets the Neighborhood: a Multifaceted 
  　　Collaborative Filtering Model) P195

【ACM RecSys 2009 Workshop】Improving recommendation accuracy by clustering so.pdf

【CIKM 2012 Best Stu Paper】Incorporating Occupancy into Frequent Pattern Mini.pdf

【CIKM 2012 poster】A Latent Pairwise Preference Learning Approach for Recomme.pdf

【CIKM 2012 poster】An Effective Category Classification Method Based on a Lan.pdf

【CIKM 2012 poster】Learning to Rank for Hybrid Recommendation.pdf

【CIKM 2012 poster】Learning to Recommend with Social Relation Ensemble.pdf

【CIKM 2012 poster】Maximizing Revenue from Strategic Recommendations under De.pdf

【CIKM 2012 poster】On Using Category Experts for Improving the Performance an.pdf

【CIKM 2012 poster】Relation Regularized Subspace Recommending for Related Sci.pdf

【CIKM 2012 poster】Top-N Recommendation through Belief Propagation.pdf

【CIKM 2012 poster】Twitter Hyperlink Recommendation with User-Tweet-Hyperlink.pdf

【CIKM 2012 short】Automatic Query Expansion Based on Tag Recommendation.pdf