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大数据-机器学习入门

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大数据/数据挖掘/推荐系统/机器学习相关资源

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

推荐系统

http://en.wikipedia.org/wiki/Information_overload
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=58http://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

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

欢迎贡献资源~~待续

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