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你可能喜欢推荐系统（资料大全）
http://blog.csdn.net/zhoulv2000/article/details/
原文来自：
刚在一篇个人博客中看到这篇文章，不知是否本论坛或者群的牛人所作，因这里面讲到了推荐系统群，故有此一说，想来作者应该不介意转到这里供家学习下，如有不妥，还望作者海涵。原文的开源推荐系统没有列出SVDFeature，但在推荐系统开源软件列表汇总和评点&&这个链接中给出来了
原文如下：
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
浙大数据挖掘系列
用Python做科学计算
Hadoop视频
42区 . 技术 .
创业 . 第二讲
加州理工学院公开课：机器学习与数据挖掘
书籍各种书~各种ppt~更新中~
机器学习经典书籍小结
QQ群机器学习&模式识别
数据挖掘机器学习
博客推荐系统周涛&
Greg Linden&&
Marcel Caraciolo& &
ResysChina& &&
推荐系统人人小站&
guwendong&
free mind&
lovebingkuai&
LeftNotEasy&
LSRS 2013&&
Google小组&
机器学习Journal of
Machine Learning Research&
信息检索清华大学信息检索组&
自然语言处理我爱自然语言处理&test
Github推荐系统推荐系统开源软件列表汇总和评点&
Mrec(Python)
Crab(Python)
Python-recsys(Python)
CofiRank(C++)
GraphLab(C++)
EasyRec(Java)
Lenskit(Java)
Mahout(Java)
Recommendable(Ruby)
文章机器学习
心中永远的正能量&&
Netflix 推荐系统：第一部分&
Netflix 推荐系统：第二部分&
探索推荐引擎内部的秘密&
推荐系统resys小组线下活动见闻&
Recommendation Engines Seminar Paper, Thomas Hess, 2009:
推荐引擎的总结性文章
Toward the Next Generation of Recommender Systems: A Survey of the
State-of-the-Art and Possible Extensions, Adomavicius, G.;
Tuzhilin, A., 2005&&
A Taxonomy of RecommenderAgents on the Internet, Montaner, M.;
Lopez, B.; de la Rosa, J. L., 2003
A Course in Machine Learning&
基于mahout构建社会化推荐引擎&&
个性化推荐技术漫谈&
Design of Recommender System&
How to build a recommender system&
推荐系统架构小结&&
System Architectures for Personalization and
Recommendation&
The Netflix Tech Blog&
百分点推荐引擎——从需求到架构
推荐系统 在InfoQ上的内容&&
推荐系统实时化的实践和思考&
质量保证的推荐实践&&
推荐系统的工程挑战&&
社会化推荐在人人网的应用&&
利用20%时间开发推荐引擎&&
使用Hadoop和 Mahout实现推荐引擎&
Netflix推荐系统：从评分预测到消费者法则&
《推荐系统实践》的Reference
http://en.wikipedia.org/wiki/Information_overload&
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　　 /ebooks/an introduction to search
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http://www.slideshare.net/plamere/music-recommendation-and-discovery&
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　　/instantpersonalization/&
/blog/digg-recommendation-engine-updates&
　　 (Digg Recommendation Engine Updates)
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　　 (The Learning Behind Gmail Priority
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　　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
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　　http://www.sigkdd.org/explorations/issues/9-2--Netflix-2.pdf&
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　　(Metrics for evaluating the serendipity of recommendation lists)
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　　(The effects of transparency on trust in and acceptance of a
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　　(Tutorial on robutness of recommender system)
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　　 (Five Stars Dominate Ratings) P37&
　　rmatik.uni-freiburg.de/~cziegler/BX/&
　　(Book-Crossing Dataset) P38&
　　http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html&
　　(Lastfm Dataset) P39&
　　//power_law_1/&
　　（浅谈网络世界的Power Law现象） P39&
　　http://www.grouplens.org/node/73/&
　　(MovieLens Dataset) P42&
　　/pubs/69656/tr-98-12.pdf&
　　(Empirical Analysis of Predictive Algorithms for Collaborative
Filtering) P49&
　　/1242909&
　　(Digg Vedio) P50&
　　http://glaros.dtc.umn.edu/gkhome/fetch/papers/itemrsCIKM01.pdf&
　　 (Evaluation of Item-Based Top-N Recommendation Algorithms)
　　http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf&
　　( Recommendations Item-to-Item Collaborative Filtering)
　　/2006/03/early-amazon-similarities.html&
　　 (Greg Linden Blog) P63&
　　http://www./techreports/2008/HPL-.pdf&
　　(One-Class Collaborative Filtering)
　　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)
　　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)
　　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)
　　https://www.aaai.org/ojs/index.php/aimagazine/article/view/1292&
　　 (LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale
Demographic Data) P80
　　/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&
　　/about/mgp&
　　(About The Music Genome Project) P94&
　　http://en.wikipedia.org/wiki/List_of_Music_Genome_Project_attributes&
　　(Pandora Music Genome Project Attributes)
　　/movie-genome.html&
　　(Jinni Movie Genome) P94&
　　/papers/tagsplanations_iui2009.pdf&
　　 (Tagsplanations: Explaining Recommendations Using Tags)
　　http://en.wikipedia.org/wiki/Tag_(metadata)&
　　(Tag Wikipedia) P96&
　　/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&
　　/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&
　　/pubs/73692/yihgoca-www06.pdf&
　　 (Finding Advertising Keywords on Web Pages)
　　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)
　　http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&rep=rep1&type=pdf&
　　(Folkrank: A ranking algorithm for folksonomies)
　　http://www.grouplens.org/system/files/tagommenders_numbered.pdf&
　　 (Tagommenders: Connecting Users to Items through Tags)
　　http://www.grouplens.org/system/files/group07-sen.pdf&
　　(The Quest for Quality Tags) P120&
　　(Challenge on Context-aware Movie Recommendation)
　　http://bits.//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)
　　http://staff.science.uva.nl/~kamps/ireval/papers/paper_14.pdf&
　　 (Evaluating Collaborative Filtering Over Time)
　　/places/&
　　(Hotpot) P139&
　　/archives/google_launches_recommendation_engine_for_places.php&
　　(Google Launches Hotpot, A Recommendation Engine for Places)
　　http://xavier.amatriain.net/pubs/GeolocatedRecommendations.pdf&
　　 (geolocated recommendations) P140&
　　/interactive//nyregion/-netflix-map.html&
　　(A Peek Into Netflix Queues) P141&
　　http://www.cs.umd.edu/users/meesh/420/neighbor.pdf&
　　(Distance Browsing in Spatial Databases1)
　　http://www.eng.auburn.edu/~weishinn/papers/MDM2010.pdf&
　　 (Efficient Evaluation of k-Range Nearest Neighbor Queries in Road
Networks) P143&
　　/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&
　　/external_content/untrusted_dlcp//en//pubs/archive/36371.pdf&
　　(Suggesting Friends Using the Implicit Social Graph)
　　/nielsenwire/online_mobile/friends-frenemies-why-we-add-and-remove-facebook-friends/&
　　(Friends & Frenemies: Why We Add and Remove Facebook Friends)
　　http://snap.stanford.edu/data/&
　　(Stanford Large Network Dataset Collection)
　　http://www.dai-labor.de/camra2010/&
　　(Workshop on Context-awareness in Retrieval and Recommendation)
　　p.hkbu.edu.hk/~lichen/download/p245-yuan.pdf&
　　 (Factorization vs. Regularization: Fusing
Heterogeneous&
　　Social Relationships in Top-N Recommendation)
　　q.com/news/2009/06/Twitter-Architecture/&
　　(Twitter, an Evolving Architecture)
　　/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)
　　http://www.ercim.eu/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf&
　　(Comparing Recommendations Made by Online Systems and Friends)
　　//facebook-edgerank/&
　　(EdgeRank: The Secret Sauce That Makes Facebook's News Feed Tick)
　　http://www.grouplens.org/system/files/p217-chen.pdf&
　　(Speak Little and Well: Recommending Conversations in Online
Social Streams) P158&
　　//learn-more-abou-2/&
　　(Learn more about “People You May Know”)
　　http://domino./cambridge/research.nsf/58bac2a2a6b05a005b576b3/$FILE/TR
2009.09 Make New Frends.pdf&
　　(“Make New Friends, but Keep the Old” & Recommending People on
Social Networking Sites) P164&
　　.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)
　　http://olivier.chapelle.cc/pub/DBN_www2009.pdf&
　　(A Dynamic Bayesian Network Click Model for Web Search Ranking)
　　.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)
　　http://www.cs.cmu.edu/~deepay/mywww/papers/www08-interaction.pdf&
　　(Contextual Advertising by Combining Relevance with Click
Feedback) P177&
　　/blog//recommendation-system/&
　　(Hulu 推荐系统架构) P178&
　　(MyMedia Project) P178&
　　http://www.grouplens.org/papers/pdf/www10_sarwar.pdf&
　　(item-based collaborative filtering recommendation algorithms)
　　http://www.stanford.edu/~koutrika/Readings/res/Default/billsus98learning.pdf&
　　(Learning Collaborative Information Filters)
　　http://sifter.org/~simon/journal/.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./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)
　　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./~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
& & Amazon
& & 豆瓣读书
& & 当当网
& & Google News
& & Genieo
Getprismatic&&
& & Netflix
& & MovieLens
& & Rotten Tomatoes
& & Flixster
& & 豆瓣电台
& & Lastfm
& & Pandora
& & EMusic
& & 虾米电台
& & Jing.FM
& & Youtube
& & Clciker
& & CiteULike
& & Google Reader
& & StumbleUpon
& & Wanderfly
& & TripAdvisor
& & Facebook
& & Twitter
& & Amazon
& & GetGlue
& & Strands
以上网友发言只代表其个人观点，不代表新浪网的观点或立场。