# Big-Data-Resources

**Repository Path**: civen516688/Big-Data-Resources

## Basic Information

- **Project Name**: Big-Data-Resources
- **Description**: 大数据/数据挖掘/推荐系统/机器学习相关资源
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-08-12
- **Last Updated**: 2020-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

#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&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 

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<p style="line-height:16px;">
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<p style="line-height:16px;">
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<p style="line-height:16px;">
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<p style="line-height:16px;">
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<p style="line-height:16px;">
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<p style="line-height:16px;">
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</p>
<p style="line-height:16px;">
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<p style="line-height:16px;">
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<p style="line-height:16px;">
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<p style="line-height:16px;">
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    <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Learning%20to%20Rank%20Social%20Update%20Streams.pdf&amp;id=38039"
    target="_blank">
        【SIGIR 2012】Learning to Rank Social Update Streams.pdf
    </a>
</p>
<p style="line-height:16px;">
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    target="_blank">
        【SIGIR 2012】Personalized Click Shaping through Lagrangian Duality for
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    </a>
</p>
<p style="line-height:16px;">
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        【SIGIR 2012】Predicting the Ratings of Multimedia Items for Making Personaliz.pdf
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</p>
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        【SIGIR 2012】TFMAP-Optimizing MAP for Top-N Context-aware Recommendation.pdf
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</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%90SIGIR%202012%E3%80%91What%20Reviews%20are%20Satisfactory-%20Novel%20Features%20for%20Automatic%20Help.pdf&amp;id=38043"
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        【SIGIR 2012】What Reviews are Satisfactory- Novel Features for Automatic
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</p>
<p style="line-height:16px;">
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</p>
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        【SIGKDD 2012】 RecMax- Exploiting Recommender Systems for Fun and Profit.pdf
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</p>
<p style="line-height:16px;">
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        【SIGKDD 2012】Circle-based Recommendation in Online Social Networks.pdf
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</p>
<p style="line-height:16px;">
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    <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91Cross-domain%20Collaboration%20Recommendation.pdf&amp;id=38047"
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        【SIGKDD 2012】Cross-domain Collaboration Recommendation.pdf
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</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%90SIGKDD%202012%E3%80%91Finding%20Trending%20Local%20Topics%20in%20Search%20Queries%20for%20Personaliza.pdf&amp;id=38048"
    target="_blank">
        【SIGKDD 2012】Finding Trending Local Topics in Search Queries for Personaliza.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%90SIGKDD%202012%E3%80%91GetJar%20Mobile%20Application%20Recommendations%20with%20Very%20Sparse%20Datasets.pdf&amp;id=38049"
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        【SIGKDD 2012】GetJar Mobile Application Recommendations with Very Sparse
        Datasets.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%90SIGKDD%202012%E3%80%91Incorporating%20Heterogenous%20Information%20for%20Personalized%20Tag%20Rec.pdf&amp;id=38050"
    target="_blank">
        【SIGKDD 2012】Incorporating Heterogenous Information for Personalized Tag
        Rec.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%90SIGKDD%202012%E3%80%91Learning%20Personal%2BSocial%20Latent%20Factor%20Model%20for%20Social%20Recomme.pdf&amp;id=38051"
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        【SIGKDD 2012】Learning Personal+Social Latent Factor Model for Social Recomme.pdf
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</p>
<p style="line-height:16px;">
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    <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90VLDB%202012%E3%80%91Challenging%20the%20Long%20Tail%20Recommendation.pdf&amp;id=38052"
    target="_blank">
        【VLDB 2012】Challenging the Long Tail 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%90VLDB%202012%E3%80%91Supercharging%20Recommender%20Systems%20using%20Taxonomies%20for%20Learning%20U.pdf&amp;id=38053"
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        【VLDB 2012】Supercharging Recommender Systems using Taxonomies for Learning
        U.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%90WWW%202012%20Best%20paper%E3%80%91Build%20Your%20Own%20Music%20Recommender%20by%20Modeling%20Internet%20R.pdf&amp;id=38054"
    target="_blank">
        【WWW 2012 Best paper】Build Your Own Music Recommender by Modeling Internet
        R.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%90WWW%202013%E3%80%91A%20Personalized%20Recommender%20System%20Based%20on%20User%5C%26%23039%3Bs%20Informatio.pdf&amp;id=38055"
    target="_blank">
        【WWW 2013】A Personalized Recommender System Based on User's Informatio.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%90WWW%202013%E3%80%91Diversified%20Recommendation%20on%20Graphs-Pitfalls%2C%20Measures%2C%20and%20Algorithms.pdf&amp;id=38056"
    target="_blank">
        【WWW 2013】Diversified Recommendation on Graphs-Pitfalls, Measures, and
        Algorithms.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%90WWW%202013%E3%80%91Do%20Social%20Explanations%20Work-Studying%20and%20Modeling%20the%20Effects%20of%20S.pdf&amp;id=38057"
    target="_blank">
        【WWW 2013】Do Social Explanations Work-Studying and Modeling the Effects
        of S.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%90WWW%202013%E3%80%91Generation%20of%20Coalition%20Structures%20to%20Provide%20Proper%20Groups%5C%26%23039%3B.pdf&amp;id=38058"
    target="_blank">
        【WWW 2013】Generation of Coalition Structures to Provide Proper Groups'.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%90WWW%202013%E3%80%91Learning%20to%20Recommend%20with%20Multi-Faceted%20Trust%20in%20Social%20Networks.pdf&amp;id=38059"
    target="_blank">
        【WWW 2013】Learning to Recommend with Multi-Faceted Trust in Social Networks.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%90WWW%202013%E3%80%91Multi-Label%20Learning%20with%20Millions%20of%20Labels-Recommending%20Advertis.pdf&amp;id=38060"
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        【WWW 2013】Multi-Label Learning with Millions of Labels-Recommending Advertis.pdf
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</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%90WWW%202013%E3%80%91Personalized%20Recommendation%20via%20Cross-Domain%20Triadic%20Factorization.pdf&amp;id=38061"
    target="_blank">
        【WWW 2013】Personalized Recommendation via Cross-Domain Triadic Factorization.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%90WWW%202013%E3%80%91Profile%20Deversity%20in%20Search%20and%20Recommendation.pdf&amp;id=38062"
    target="_blank">
        【WWW 2013】Profile Deversity in Search and 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%90WWW%202013%E3%80%91Real-Time%20Recommendation%20of%20Deverse%20Related%20Articles.pdf&amp;id=38063"
    target="_blank">
        【WWW 2013】Real-Time Recommendation of Deverse Related Articles.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%90WWW%202013%E3%80%91Recommendation%20for%20Online%20Social%20Feeds%20by%20Exploiting%20User%20Response.pdf&amp;id=38064"
    target="_blank">
        【WWW 2013】Recommendation for Online Social Feeds by Exploiting User Response.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%90WWW%202013%E3%80%91Recommending%20Collaborators%20Using%20Keywords.pdf&amp;id=38065"
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        【WWW 2013】Recommending Collaborators Using Keywords.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%90WWW%202013%E3%80%91Signal-Based%20User%20Recommendation%20on%20Twitter.pdf&amp;id=38066"
    target="_blank">
        【WWW 2013】Signal-Based User Recommendation on Twitter.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%90WWW%202013%E3%80%91SoCo-%20A%20Social%20Network%20Aided%20Context-Aware%20Recommender%20System.pdf&amp;id=38067"
    target="_blank">
        【WWW 2013】SoCo- A Social Network Aided Context-Aware Recommender System.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%90WWW%202013%E3%80%91Tailored%20News%20in%20the%20Palm%20of%20Your%20HAND-A%20Multi-Perspective%20Transpa.pdf&amp;id=38068"
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        【WWW 2013】Tailored News in the Palm of Your HAND-A Multi-Perspective Transpa.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%90WWW%202013%E3%80%91TopRec-Domain-Specific%20Recommendation%20through%20Community%20Topic%20Mini.pdf&amp;id=38069"
    target="_blank">
        【WWW 2013】TopRec-Domain-Specific Recommendation through Community Topic
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</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%90WWW%202013%E3%80%91User%5C%26%23039%3Bs%20Satisfaction%20in%20Recommendation%20Systems%20for%20Groups-an%20.pdf&amp;id=38070"
    target="_blank">
        【WWW 2013】User's Satisfaction in Recommendation Systems for Groups-an
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</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%90WWW%202013%E3%80%91Using%20Link%20Semantics%20to%20Recommend%20Collaborations%20in%20Academic%20Socia.pdf&amp;id=38071"
    target="_blank">
        【WWW 2013】Using Link Semantics to Recommend Collaborations in Academic
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</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%90WWW%202013%E3%80%91Whom%20to%20Mention-Expand%20the%20Diffusion%20of%20Tweets%20by%20%40%20Recommendation.pdf&amp;id=38072"
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        【WWW 2013】Whom to Mention-Expand the Diffusion of Tweets by @ Recommendation.pdf
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</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=Recommender%2BSystems%2BHandbook.pdf&amp;id=38073"
    target="_blank">
        Recommender+Systems+Handbook.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=tutorial.pdf&amp;id=38074"
    target="_blank">
        tutorial.pdf
    </a>
</p>



##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



##欢迎贡献资源~~待续