推荐系统资料汇总

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 ***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., 2003http://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 introduction to search engines and web navigation.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 Trust-aware recommender systems.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://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4072747  　　(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–Leibler_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 dataset dai-larbor&source=web&cd=1&ved=0CFIQFjAA&url=http://www.dai-labor.de/en/competence_centers/irml/datasets/&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://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.3679&rep=rep1&type=pdf&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 2009.09 Make New Frends.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://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.141.465&rep=rep1&type=pdf&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://www.research.yahoo.net/files/p227-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 复制代码

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