推荐系统资料大全（部分）

来源：互联网发布：简明python教程电子书编辑：程序博客网时间：2024/05/16 23:48

原文地址：http://my.oschina.net/u/1180306/blog/300602

## 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/>

在线的机器学习社区 <http://www.52ml.net/16336.html>

###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/>

##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>

* 机器学习最佳入门学习资料汇总 <http://article.yeeyan.org/view/22139/410514>

* Books for Machine Learning with R <http://www.52ml.net/16312.html>

* 是什么阻碍了你的机器学习目标？ <http://www.52ml.net/16436.htm>

###推荐系统

* 推荐系统初探 <http://yongfeng.me/attach/rs-survey-zhang-slices.pdf>

* 推荐系统中协同过滤算法若干问题的研究 <http://pan.baidu.com/s/1bnjDBYZ>

* 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

* [P1](http://en.wikipedia.org/wiki/Information_overload)

* [A Guide to Recommender System P4](http://www.readwriteweb.com/archives/recommender_systems.php)

* [Cross Selling P6](http://en.wikipedia.org/wiki/Cross-selling)

* [课程：Data Mining and E-Business: The Social Data Revolution P7)](http://stanford2009.wikispaces.com/ )

* [An Introduction to Search Engines and Web Navigation p7](http://thesearchstrategy.com/ebooks/an%20introduction%20to%20search%20engines%20and%20web%20navigation.pdf)
　　
* [p8](http://www.netflixprize.com/)

* [p9](http://cdn-0.nflximg.com/us/pdf/Consumer_Press_Kit.pdf)

* [(The Youtube video recommendation system) p9](http://stuyresearch.googlecode.com/hg-history/c5aa9d65d48c787fd72dcd0ba3016938312102bd/blake/resources/p293-davidson.pdf)
　　
* [(PPT: Music Recommendation and Discovery) p12](http://www.slideshare.net/plamere/music-recommendation-and-discovery)

* [P13](http://www.facebook.com/instantpersonalization/)
　　
* [(Digg Recommendation Engine Updates) P16](http://about.digg.com/blog/digg-recommendation-engine-updates)

* [(The Learning Behind Gmail Priority Inbox)p17](http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36955.pdf)
　　
* [(Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20](http://www.grouplens.org/papers/pdf/mcnee-chi06-acc.pdf)

* [(Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23](http://www-users.cs.umn.edu/~mcnee/mcnee-cscw2006.pdf)
　　
* [(Major componets of the gravity recommender system) P25](http://www.sigkdd.org/explorations/issues/9-2-2007-12/7-Netflix-2.pdf)

* [(What is a Good Recomendation Algorithm?) P26](http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext)

* [(Evaluation Recommendation Systems) P27](http://research.microsoft.com/pubs/115396/evaluationmetrics.tr.pdf )
　　
* [(Music Recommendation and Discovery in the Long Tail) P29](http://mtg.upf.edu/static/media/PhD_ocelma.pdf)
　　

* [(Internation Workshop on Novelty and Diversity in Recommender Systems) p29](http://ir.ii.uam.es/divers2011/)

* [(Auralist: Introducing Serendipity into Music Recommendation ) P30](http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_21.pdf)
　　
* [(Metrics for evaluating the serendipity of recommendation lists) P30](http://www.springerlink.com/content/978-3-540-78196-7/#section=239197&page=1&locus=21)

* [(The effects of transparency on trust in and acceptance of a content-based art recommender) P31](http://dare.uva.nl/document/131544)
　　
* [(Trust-aware recommender systems) P31](http://brettb.net/project/papers/2007%20Trust-aware%20recommender%20systems.pdf)

* [(Tutorial on robutness of recommender system) P32](http://recsys.acm.org/2011/pdfs/RobustTutorial.pdf)

* [(Five Stars Dominate Ratings) P37 ](http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html)

* [(Book-Crossing Dataset) P38 ](http://www.informatik.uni-freiburg.de/~cziegler/BX/)

* [(Lastfm Dataset) P39](http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html)
　　
* [浅谈网络世界的Power Law现象 P39](http://mmdays.com/2008/11/22/power_law_1/)

* [(MovieLens Dataset) P42](http://www.grouplens.org/node/73/)

* [(Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49](http://research.microsoft.com/pubs/69656/tr-98-12.pdf)

* [(Digg Vedio) P50](http://vimeo.com/1242909)

* [(Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59](http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf)

* [(Greg Linden Blog) P63](http://glinden.blogspot.com/2006/03/early-amazon-similarities.html)

* [(One-Class Collaborative Filtering) P67](http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf)

* [(Stochastic Gradient Descent) P68 ](http://en.wikipedia.org/wiki/Stochastic_gradient_descent)

* [(Latent Factor Models for Web Recommender Systems) P70 ](http://www.ideal.ece.utexas.edu/seminar/LatentFactorModels.pdf)

* [(Bipatite Graph) P73](http://en.wikipedia.org/wiki/Bipartite_graph)

* [(Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4072747&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4072747)

* [(Topic Sensitive Pagerank) P74](http://www-cs-students.stanford.edu/~taherh/papers/topic-sensitive-pagerank.pdf)

* [(FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77](http://www.stanford.edu/dept/ICME/docs/thesis/Li-2009.pdf)

* [(LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80](https://www.aaai.org/ojs/index.php/aimagazine/article/view/1292)

* [( adaptive bootstrapping of recommender systems using decision trees) P87](http://research.yahoo.com/files/wsdm266m-golbandi.pdf)

* [(Vector Space Model) P90](http://en.wikipedia.org/wiki/Vector_space_model)

* [(冷启动问题的比赛) P92](http://tunedit.org/challenge/VLNetChallenge )

* [(Latent Dirichlet Allocation) P92](http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf)

* [(Kullback–Leibler divergence) P93](http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)

* [(About The Music Genome Project) P94](http://www.pandora.com/about/mgp)

* [(Pandora Music Genome Project Attributes) P94](http://en.wikipedia.org/wiki/List_of_Music_Genome_Project_attributes )

* [(Jinni Movie Genome) P94](http://www.jinni.com/movie-genome.html)

* [(Tagsplanations: Explaining Recommendations Using Tags) P96](http://www.shilad.com/papers/tagsplanations_iui2009.pdf)

* [(Tag Wikipedia) P96](http://en.wikipedia.org/wiki/Tag_(metadata))

* [(Nurturing Tagging Communities) P100](http://www.shilad.com/shilads_thesis.pdf )

* [(Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100](http://www.stanford.edu/~morganya/research/chi2007-tagging.pdf )

* [(Delicious Dataset) P101](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)

* [(Finding Advertising Keywords on Web Pages) P118](http://research.microsoft.com/pubs/73692/yihgoca-www06.pdf )

* [(基于标签的推荐系统比赛) P119](http://www.kde.cs.uni-kassel.de/ws/rsdc08/ )

* [(Tag recommendations based on tensor dimensionality reduction）P119](http://delab.csd.auth.gr/papers/recsys.pdf)

* [(latent dirichlet allocation for tag recommendation) P119](http://www.l3s.de/web/upload/documents/1/recSys09.pdf)

* [(Folkrank: A ranking algorithm for folksonomies) P119](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&rep=rep1&type=pdf)

* [(Tagommenders: Connecting Users to Items through Tags) P119](http://www.grouplens.org/system/files/tagommenders_numbered.pdf)

* [(The Quest for Quality Tags) P120](http://www.grouplens.org/system/files/group07-sen.pdf)

* [(Challenge on Context-aware Movie Recommendation) P123](http://2011.camrachallenge.com/)

* [(The Lifespan of a link) P125](http://bits.blogs.nytimes.com/2011/09/07/the-lifespan-of-a-link/ )

* [(Temporal Diversity in Recommender Systems) P129](http://www0.cs.ucl.ac.uk/staff/l.capra/publications/lathia_sigir10.pdf )

* [(Evaluating Collaborative Filtering Over Time) P129](http://staff.science.uva.nl/~kamps/ireval/papers/paper_14.pdf )

* [(Hotpot) P139 ](http://www.google.com/places/ )

* [(Google Launches Hotpot, A Recommendation Engine for Places) P139](http://www.readwriteweb.com/archives/google_launches_recommendation_engine_for_places.php)

* [(geolocated recommendations) P140](http://xavier.amatriain.net/pubs/GeolocatedRecommendations.pdf )

* [(A Peek Into Netflix Queues) P141](http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html )

* [(Distance Browsing in Spatial Databases1) P142](http://www.cs.umd.edu/users/meesh/420/neighbor.pdf )

* [(Efﬁcient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143](http://www.eng.auburn.edu/~weishinn/papers/MDM2010.pdf )

* [(Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144 ](http://blog.nielsen.com/nielsenwire/consumer/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most/ )

* [(Suggesting Friends Using the Implicit Social Graph) P145](http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36371.pdf )

* [(Friends & Frenemies: Why We Add and Remove Facebook Friends) P147](http://blog.nielsen.com/nielsenwire/online_mobile/friends-frenemies-why-we-add-and-remove-facebook-friends/ )

* [(Stanford Large Network Dataset Collection) P149 ](http://snap.stanford.edu/data/ )

* [(Workshop on Context-awareness in Retrieval and Recommendation) P151](http://www.dai-labor.de/camra2010/ )

* [(Factorization vs. Regularization: Fusing Heterogeneous Social Relationships in Top-N Recommendation) P153 ](http://www.comp.hkbu.edu.hk/~lichen/download/p245-yuan.pdf )

* [(Twitter, an Evolving Architecture) P154](http://www.infoq.com/news/2009/06/Twitter-Architecture/ )

* [(Recommendations in taste related domains) P155](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 )

* [(Comparing Recommendations Made by Online Systems and Friends) P155](http://www.ercim.eu/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf)

* [(EdgeRank: The Secret Sauce That Makes Facebook's News Feed Tick) P157](http://techcrunch.com/2010/04/22/facebook-edgerank/ )

* [(Speak Little and Well: Recommending Conversations in Online Social Streams) P158] (http://www.grouplens.org/system/files/p217-chen.pdf )

* [(Learn more about “People You May Know”) P160](http://blog.linkedin.com/2008/04/11/learn-more-abou-2/ )

* [("Make New Friends, but Keep the Old" – Recommending People on Social Networking Sites) P164 ](http://domino.watson.ibm.com/cambridge/research.nsf/58bac2a2a6b05a1285256b30005b3953/8186a48526821924852576b300537839/$FILE/TR%202009.09%20Make%20New%20Frends.pdf)

* [(SoRec: Social Recommendation Using Probabilistic Matrix) P165 ](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)

* [(A Dynamic Bayesian Network Click Model for Web Search Ranking) P177](http://olivier.chapelle.cc/pub/DBN_www2009.pdf )

* [(Online Learning from Click Data for Sponsored Search) 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)

* [(Contextual Advertising by Combining Relevance with Click Feedback) P177 ](http://www.cs.cmu.edu/~deepay/mywww/papers/www08-interaction.pdf)

* [(Hulu 推荐系统架构) P178](http://tech.hulu.com/blog/2011/09/19/recommendation-system/ )

* [(MyMedia Project) P178](http://mymediaproject.codeplex.com/)

* [(item-based collaborative filtering recommendation algorithms) P185](http://www.grouplens.org/papers/pdf/www10_sarwar.pdf )

* [(Learning Collaborative Information Filters) P186 ](http://www.stanford.edu/~koutrika/Readings/res/Default/billsus98learning.pdf )

* [(Simon Funk Blog:Funk SVD) P187 ](http://sifter.org/~simon/journal/20061211.html )

* [(Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190 ](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf )

* [(Time-dependent Models in Collaborative Filtering based Recommender System) P193 ](http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh26.pdf )

* [(Collaborative filtering with temporal dynamics) P193](http://sydney.edu.au/engineering/it/~josiah/lemma/kdd-fp074-koren.pdf )

* [(Least Squares Wikipedia) P195](http://en.wikipedia.org/wiki/Least_squares )

* [(Improving regularized singular value decomposition for collaborative filtering) P195](http://www.mimuw.edu.pl/~paterek/ap_kdd.pdf )

* [(Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model) P195](http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf )

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