大数据

大数据/数据挖掘/推荐系统/机器学习相关资源
Share my personal resources
#视频
###大数据视频以及讲义 http://pan.baidu.com/share/link?shareid=3860301827&uk=3978262348
###浙大数据挖掘系列 http://v.youku.com/v_show/id_XNTgzNDYzMjg=.html?f=2740765
###用Python做科学计算 http://www.tudou.com/listplay/fLDkg5e1pYM.html
###R语言视频 http://pan.baidu.com/s/1koSpZ
###Hadoop视频 http://pan.baidu.com/s/1b1xYd
###42区 . 技术 . 创业 . 第二讲 http://v.youku.com/v_show/id_XMzAyMDYxODUy.html
###加州理工学院公开课：机器学习与数据挖掘 http://v.163.com/special/opencourse/learningfromdata.html
=======================
##书籍
###各种书
各种ppt更新中~ http://pan.baidu.com/s/1EaLnZ
###机器学习经典书籍小结 http://www.cnblogs.com/snake-hand/archive/2013/06/10/3131145.html
=======================
##QQ群
机器学习&模式识别 246159753
数据挖掘机器学习 236347059
推荐系统 274750470

博客

###推荐系统
周涛 http://blog.sciencenet.cn/home.php?mod=space&uid=3075
Greg Linden http://glinden.blogspot.com/
Marcel Caraciolo http://aimotion.blogspot.com/
ResysChina http://weibo.com/p/1005051686952981
推荐系统人人小站 http://zhan.renren.com/recommendersystem
阿稳 http://www.wentrue.net
梁斌 http://weibo.com/pennyliang
刁瑞 http://diaorui.net
guwendong http://www.guwendong.com
xlvector http://xlvector.net
懒惰啊我 http://www.cnblogs.com/flclain/
free mind http://blog.pluskid.org/
lovebingkuai http://lovebingkuai.diandian.com/
LeftNotEasy http://www.cnblogs.com/LeftNotEasy
LSRS 2013 http://graphlab.org/lsrs2013/program/
Google小组 https://groups.google.com/forum/#!forum/resys
###机器学习
Journal of Machine Learning Research http://jmlr.org/
###信息检索
清华大学信息检索组 http://www.thuir.cn
###自然语言处理
我爱自然语言处理 http://www.52nlp.cn/ test ##Github
###推荐系统
推荐系统开源软件列表汇总和评点 http://in.sdo.com/?p=1707
Mrec(Python)
https://github.com/mendeley/mrec
Crab(Python)
https://github.com/muricoca/crab
Python-recsys(Python)
https://github.com/ocelma/python-recsys
CofiRank(C++)
https://github.com/markusweimer/cofirank
GraphLab(C++)
https://github.com/graphlab-code/graphlab
EasyRec(Java)
https://github.com/hernad/easyrec
Lenskit(Java)
https://github.com/grouplens/lenskit
Mahout(Java)
https://github.com/apache/mahout
Recommendable(Ruby)
https://github.com/davidcelis/recommendable
##文章
###机器学习

• 心中永远的正能量 http://blog.csdn.net/yunlong34574

###推荐系统

• 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&source=web&cd=1&ved=0CFIQFjAA&url=http%3A%2F%2Fwww.dai-labor.de%2Fen%2Fcompetence_centers%2Firml%2Fdatasets%2F&ei=1R4JUKyFOKu0iQfKvazzCQ&;usg=AFQjCNGuVzzKIKi3K2YFybxrCNxbtKqS4A&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&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&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&cd=2&ved=0CFcQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.141.465%26rep%3Drep1%26type%3Dpdf&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&source=web&cd=1&ved=0CFkQFjAA&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
【CIKM 2012 short】Graph-Based Workflow Recommendation- On Improving Business .pdf
【CIKM 2012 short】Location-Sensitive Resources Recommendation in Social Taggi.pdf
【CIKM 2012 short】More Than Relevance- High Utility Query Recommendation By M.pdf
【CIKM 2012 short】PathRank- A Novel Node Ranking Measure on a Heterogeneous G.pdf
【CIKM 2012 short】PRemiSE- Personalized News Recommendation via Implicit Soci.pdf
【CIKM 2012 short】Query Recommendation for Children.pdf
【CIKM 2012 short】The Early-Adopter Graph and its Application to Web-Page Rec.pdf
【CIKM 2012 short】Time-aware Topic Recommendation Based on Micro-blogs.pdf
【CIKM 2012 short】Using Program Synthesis for Social Recommendations.pdf
【CIKM 2012】A Decentralized Recommender System for Effective Web Credibility .pdf
【CIKM 2012】A Generalized Framework for Reciprocal Recommender Systems.pdf
【CIKM 2012】Dynamic Covering for Recommendation Systems.pdf
【CIKM 2012】Efficient Retrieval of Recommendations in a Matrix Factorization .pdf
【CIKM 2012】Exploring Personal Impact for Group Recommendation.pdf
【CIKM 2012】LogUCB- An Explore-Exploit Algorithm For Comments Recommendation.pdf
【CIKM 2012】Metaphor- A System for Related Search Recommendations.pdf
【CIKM 2012】Social Contextual Recommendation.pdf
【CIKM 2012】Social Recommendation Across Multiple Relational Domains.pdf
【COMMUNICATIONS OF THE ACM】Recommender Systems.pdf
【ICDM 2012 short___】Multiplicative Algorithms for Constrained Non-negative M.pdf
【ICDM 2012 short】Collaborative Filtering with Aspect-based Opinion Mining- A.pdf
【ICDM 2012 short】Learning Heterogeneous Similarity Measures for Hybrid-Recom.pdf
【ICDM 2012 short】Mining Personal Context-Aware Preferences for Mobile Users.pdf
【ICDM 2012】Link Prediction and Recommendation across Heterogenous Social Networks.pdf
【IEEE Computer Society 2009】Matrix factorization techniques for recommender .pdf
【IEEE Consumer Communications and Networking Conference 2006】FilmTrust movie.pdf
【IEEE Trans on Audio, Speech and Laguage Processing 2010】Personalized music .pdf
【IEEE Transactions on Knowledge and Data Engineering 2005】Toward the next ge.pdf
【INFOCOM 2011】Bayesian-inference Based Recommendation in Online Social Network.pdf
【KDD 2009】Learning optimal ranking with tensor factorization for tag recomme.pdf
【SIGIR 2009】Learning to Recommend with Social Trust Ensemble.pdf
【SIGIR 2012】Adaptive Diversification of Recommendation Results via Latent Fa.pdf
【SIGIR 2012】Collaborative Personalized Tweet Recommendation.pdf
【SIGIR 2012】Dual Role Model for Question Recommendation in Community Questio.pdf
【SIGIR 2012】Exploring Social Influence for Recommendation - A Generative Mod.pdf
【SIGIR 2012】Increasing Temporal Diversity with Purchase Intervals.pdf
【SIGIR 2012】Learning to Rank Social Update Streams.pdf
【SIGIR 2012】Personalized Click Shaping through Lagrangian Duality for Online.pdf
【SIGIR 2012】Predicting the Ratings of Multimedia Items for Making Personaliz.pdf
【SIGIR 2012】TFMAP-Optimizing MAP for Top-N Context-aware Recommendation.pdf
【SIGIR 2012】What Reviews are Satisfactory- Novel Features for Automatic Help.pdf
【SIGKDD 2012】 A Semi-Supervised Hybrid Shilling Attack Detector for Trustwor.pdf
【SIGKDD 2012】 RecMax- Exploiting Recommender Systems for Fun and Profit.pdf
【SIGKDD 2012】Circle-based Recommendation in Online Social Networks.pdf
【SIGKDD 2012】Cross-domain Collaboration Recommendation.pdf
【SIGKDD 2012】Finding Trending Local Topics in Search Queries for Personaliza.pdf
【SIGKDD 2012】GetJar Mobile Application Recommendations with Very Sparse Datasets.pdf
【SIGKDD 2012】Incorporating Heterogenous Information for Personalized Tag Rec.pdf
【SIGKDD 2012】Learning Personal+Social Latent Factor Model for Social Recomme.pdf
【VLDB 2012】Challenging the Long Tail Recommendation.pdf
【VLDB 2012】Supercharging Recommender Systems using Taxonomies for Learning U.pdf
【WWW 2012 Best paper】Build Your Own Music Recommender by Modeling Internet R.pdf
【WWW 2013】A Personalized Recommender System Based on User's Informatio.pdf
【WWW 2013】Diversified Recommendation on Graphs-Pitfalls, Measures, and Algorithms.pdf
【WWW 2013】Do Social Explanations Work-Studying and Modeling the Effects of S.pdf
【WWW 2013】Generation of Coalition Structures to Provide Proper Groups'.pdf
【WWW 2013】Learning to Recommend with Multi-Faceted Trust in Social Networks.pdf
【WWW 2013】Multi-Label Learning with Millions of Labels-Recommending Advertis.pdf
【WWW 2013】Personalized Recommendation via Cross-Domain Triadic Factorization.pdf
【WWW 2013】Profile Deversity in Search and Recommendation.pdf
【WWW 2013】Real-Time Recommendation of Deverse Related Articles.pdf
【WWW 2013】Recommendation for Online Social Feeds by Exploiting User Response.pdf
【WWW 2013】Recommending Collaborators Using Keywords.pdf
【WWW 2013】Signal-Based User Recommendation on Twitter.pdf
【WWW 2013】SoCo- A Social Network Aided Context-Aware Recommender System.pdf
【WWW 2013】Tailored News in the Palm of Your HAND-A Multi-Perspective Transpa.pdf
【WWW 2013】TopRec-Domain-Specific Recommendation through Community Topic Mini.pdf
【WWW 2013】User's Satisfaction in Recommendation Systems for Groups-an .pdf
【WWW 2013】Using Link Semantics to Recommend Collaborations in Academic Socia.pdf
【WWW 2013】Whom to Mention-Expand the Diffusion of Tweets by @ Recommendation.pdf
Recommender+Systems+Handbook.pdf
tutorial.pdf
##各个领域的推荐系统
图书

• Amazon
• 豆瓣读书
• 当当网

新闻

• Google News
• Genieo
• Getprismatic http://getprismatic.com/

电影

• Netflix
• Jinni
• MovieLens
• Rotten Tomatoes
• Flixster
• MTime

音乐

• 豆瓣电台
• Lastfm
• Pandora
• Mufin
• Lala
• EMusic
• Ping
• 虾米电台
• Jing.FM

视频

• Youtube
• Hulu
• Clciker

文章

• CiteULike
• Google Reader
• StumbleUpon

旅游

• Wanderfly
• TripAdvisor

社会网络

• Facebook
• Twitter

综合

• Amazon
• GetGlue
• Strands
• Hunch

from
https://github.com/weiweifan/Big-Data-Resources