92KM92KM

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大数据-机器学习入门

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 

<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90ACM%20RecSys%202009%20Workshop%E3%80%91Improving%20recommendation%20accuracy%20by%20clustering%20so.pdf&amp;id=37991"
target="_blank">
    【ACM RecSys 2009 Workshop】Improving recommendation accuracy by clustering
    so.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20Best%20Stu%20Paper%E3%80%91Incorporating%20Occupancy%20into%20Frequent%20Pattern%20Mini.pdf&amp;id=37992"
target="_blank">
    【CIKM 2012 Best Stu Paper】Incorporating Occupancy into Frequent Pattern
    Mini.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91A%20Latent%20Pairwise%20Preference%20Learning%20Approach%20for%20Recomme.pdf&amp;id=37993"
target="_blank">
    【CIKM 2012 poster】A Latent Pairwise Preference Learning Approach for Recomme.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91An%20Effective%20Category%20Classification%20Method%20Based%20on%20a%20Lan.pdf&amp;id=37994"
target="_blank">
    【CIKM 2012 poster】An Effective Category Classification Method Based on
    a Lan.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Learning%20to%20Rank%20for%20Hybrid%20Recommendation.pdf&amp;id=37995"
target="_blank">
    【CIKM 2012 poster】Learning to Rank for Hybrid Recommendation.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Learning%20to%20Recommend%20with%20Social%20Relation%20Ensemble.pdf&amp;id=37996"
target="_blank">
    【CIKM 2012 poster】Learning to Recommend with Social Relation Ensemble.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Maximizing%20Revenue%20from%20Strategic%20Recommendations%20under%20De.pdf&amp;id=37997"
target="_blank">
    【CIKM 2012 poster】Maximizing Revenue from Strategic Recommendations under
    De.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91On%20Using%20Catexperts%20for%20Improving%20the%20Performance%20an.pdf&amp;id=37998"
target="_blank">
    【CIKM 2012 poster】On Using Category Experts for Improving the Performance
    an.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Relation%20Regularized%20Subspace%20Recommending%20for%20Related%20Sci.pdf&amp;id=37999"
target="_blank">
    【CIKM 2012 poster】Relation Regularized Subspace Recommending for Related
    Sci.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Top-N%20Recommendation%20through%20Belief%20Propagation.pdf&amp;id=38000"
target="_blank">
    【CIKM 2012 poster】Top-N Recommendation through Belief Propagation.pdf
</a>

</p>
<p style="line-height:16px;">

<img src="/static/ueditor/dialogs/attachment/fileTypeImages/icon_default.png">
<a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Twitter%20Hyperlink%20Recommendation%20with%20User-Tweet-Hyperlink.pdf&amp;id=38001"
target="_blank">
    【CIKM 2012 poster】Twitter Hyperlink Recommendation with User-Tweet-Hyperlink.pdf
</a>

</p>
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