机器学习相关资源

小咪咪 2022-07-28 11:51 333阅读 0赞

**非常感谢git上的大牛整理的机器学习资源**  
**转**[https://github.com/Flowerowl/Big\_Data\_Resources][https_github.com_Flowerowl_Big_Data_Resources]

# 大数据/数据挖掘/推荐系统/机器学习相关资源 #

Share my personal resources

# 书籍 #

*  各种书~各种ppt~更新中~ [http://pan.baidu.com/s/1EaLnZ][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][http_www.cnblogs.com_snake-hand_archive_2013_06_10_3131145.html]
 *  机器学习&深度学习经典资料汇总 [http://www.thebigdata.cn/JiShuBoKe/13299.html][http_www.thebigdata.cn_JiShuBoKe_13299.html]

# 视频 #

*  浙大数据挖掘系列 [http://v.youku.com/v\_show/id\_XNTgzNDYzMjg=.html?f=2740765][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][http_www.tudou.com_listplay_fLDkg5e1pYM.html]
 *  R语言视频 [http://pan.baidu.com/s/1koSpZ][http_pan.baidu.com_s_1koSpZ]
 *  Hadoop视频 [http://pan.baidu.com/s/1b1xYd][http_pan.baidu.com_s_1b1xYd]
 *  42区 . 技术 . 创业 . 第二讲 [http://v.youku.com/v\_show/id\_XMzAyMDYxODUy.html][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][http_v.163.com_special_opencourse_learningfromdata.html]
 *  William Huang教授–BI和数据挖掘技术 [https://www.youtube.com/watch?v=gPm8SOJ1Nao][https_www.youtube.com_watch_v_gPm8SOJ1Nao]
 *  Jingyuan Wang王静远-北航-机器学习工具在城市数据分析中的应用[https://www.youtube.com/watch?v=H2\_BATE8uok][https_www.youtube.com_watch_v_H2_BATE8uok]
 *  Tensor Flow 介紹 (1/2)[https://www.youtube.com/watch?v=jA4Bh-xj\_mE][https_www.youtube.com_watch_v_jA4Bh-xj_mE]
 *  Tensor Flow 介紹 (2/2)[https://www.youtube.com/watch?v=YUCsOgkFqpk][https_www.youtube.com_watch_v_YUCsOgkFqpk]

# 课程 #

### 机器学习 ###

*  Machine Learning Stanford University [https://www.coursera.org/learn/machine-learning][https_www.coursera.org_learn_machine-learning]
 *  機器學習基石 (Machine Learning Foundations) National Taiwan University [https://www.coursera.org/course/ntumlone][https_www.coursera.org_course_ntumlone]

# 会议 #

### 数据挖掘 ###

*  ACM SigKDD [http://www.kdd.org][http_www.kdd.org]
 *  ICDM [http://icdm2015.stonybrook.edu/][http_icdm2015.stonybrook.edu]
 *  SIAM [http://www.siam.org/][http_www.siam.org]

# 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

# 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][http_in.sdo.com_p_1707]
 *  Mrec(Python) [https://github.com/mendeley/mrec][https_github.com_mendeley_mrec]
 *  Crab(Python) [https://github.com/muricoca/crab][https_github.com_muricoca_crab]
 *  Python-recsys(Python) [https://github.com/ocelma/python-recsys][https_github.com_ocelma_python-recsys]
 *  CofiRank(C++) [https://github.com/markusweimer/cofirank][https_github.com_markusweimer_cofirank]
 *  GraphLab(C++) [https://github.com/graphlab-code/graphlab][https_github.com_graphlab-code_graphlab]
 *  EasyRec(Java) [https://github.com/hernad/easyrec][https_github.com_hernad_easyrec]
 *  Lenskit(Java) [https://github.com/grouplens/lenskit][https_github.com_grouplens_lenskit]
 *  Mahout(Java) [https://github.com/apache/mahout][https_github.com_apache_mahout]
 *  Recommendable(Ruby) [https://github.com/davidcelis/recommendable][https_github.com_davidcelis_recommendable]

### 库 ###

*  NLTK [https://github.com/nltk/nltk][https_github.com_nltk_nltk]
 *  Pattern [https://github.com/clips/pattern][https_github.com_clips_pattern]
 *  Pyrallel [https://github.com/pydata/pyrallel][https_github.com_pydata_pyrallel]
 *  Theano [https://github.com/Theano/Theano][https_github.com_Theano_Theano]
 *  Pylearn2 [https://github.com/lisa-lab/pylearn2][https_github.com_lisa-lab_pylearn2]
 *  TextBlob [https://github.com/sloria/TextBlob][https_github.com_sloria_TextBlob]
 *  MBSP [https://github.com/clips/MBSP][https_github.com_clips_MBSP]
 *  Gensim [https://github.com/piskvorky/gensim][https_github.com_piskvorky_gensim]
 *  Langid.py [https://github.com/saffsd/langid.py][https_github.com_saffsd_langid.py]
 *  Jieba [https://github.com/fxsjy/jieba][https_github.com_fxsjy_jieba]
 *  xTAS [https://github.com/NLeSC/xtas][https_github.com_NLeSC_xtas]
 *  NumPy [https://github.com/numpy/numpy][https_github.com_numpy_numpy]
 *  SciPy [https://github.com/scipy/scipy][https_github.com_scipy_scipy]
 *  Matplotlib [https://github.com/matplotlib/matplotlib][https_github.com_matplotlib_matplotlib]
 *  scikit-learn [https://github.com/scikit-learn/scikit-learn][https_github.com_scikit-learn_scikit-learn]
 *  Pandas [https://github.com/pydata/pandas][https_github.com_pydata_pandas]
 *  MDP [http://mdp-toolkit.sourceforge.net/][http_mdp-toolkit.sourceforge.net]
 *  PyBrain [https://github.com/pybrain/pybrain][https_github.com_pybrain_pybrain]
 *  PyML [http://pyml.sourceforge.net/][http_pyml.sourceforge.net]
 *  Milk [https://github.com/luispedro/milk][https_github.com_luispedro_milk]
 *  PyMVPA [https://github.com/PyMVPA/PyMVPA][https_github.com_PyMVPA_PyMVPA]
 *  TensorFlow [https://github.com/tensorflow/tensorflow][https_github.com_tensorflow_tensorflow]

# 博客 #

*  周涛 [http://blog.sciencenet.cn/home.php?mod=space&uid=3075][http_blog.sciencenet.cn_home.php_mod_space_uid_3075]
 *  Greg Linden [http://glinden.blogspot.com/][http_glinden.blogspot.com]
 *  Marcel Caraciolo [http://aimotion.blogspot.com/][http_aimotion.blogspot.com]
 *  RsysChina [http://weibo.com/p/1005051686952981][http_weibo.com_p_1005051686952981]
 *  推荐系统人人小站 [http://zhan.renren.com/recommendersystem][http_zhan.renren.com_recommendersystem]
 *  阿稳 [http://www.wentrue.net][http_www.wentrue.net]
 *  梁斌 [http://weibo.com/pennyliang][http_weibo.com_pennyliang]
 *  刁瑞 [http://diaorui.net][http_diaorui.net]
 *  guwendong [http://www.guwendong.com][http_www.guwendong.com]
 *  xlvector [http://xlvector.net][http_xlvector.net]
 *  懒惰啊我 [http://www.cnblogs.com/flclain/][http_www.cnblogs.com_flclain]
 *  free mind [http://blog.pluskid.org/][http_blog.pluskid.org]
 *  lovebingkuai [http://lovebingkuai.diandian.com/][http_lovebingkuai.diandian.com]
 *  LeftNotEasy [http://www.cnblogs.com/LeftNotEasy][http_www.cnblogs.com_LeftNotEasy]
 *  LSRS 2013 [http://graphlab.org/lsrs2013/program/][http_graphlab.org_lsrs2013_program]
 *  Google小组 [https://groups.google.com/forum/\#!forum/resys][https_groups.google.com_forum_forum_resys]
 *  Journal of Machine Learning Research [http://jmlr.org/][http_jmlr.org]
 *  在线的机器学习社区 [http://www.52ml.net/16336.html][http_www.52ml.net_16336.html]
 *  清华大学信息检索组 [http://www.thuir.cn][http_www.thuir.cn]
 *  我爱自然语言处理 [http://www.52nlp.cn/][http_www.52nlp.cn]
 *  数据挖掘与数据分析[http://spss-market.r.blog.163.com/][http_spss-market.r.blog.163.com]

# 文章 #

*  心中永远的正能量 [http://blog.csdn.net/yunlong34574][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][http_article.yeeyan.org_view_22139_410514]
 *  Books for Machine Learning with R [http://www.52ml.net/16312.html][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][http_www.52ml.net_16436.htm]
 *  推荐系统初探 [http://yongfeng.me/attach/rs-survey-zhang-slices.pdf][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][http_pan.baidu.com_s_1bnjDBYZ]
 *  Netflix 推荐系统：第一部分 [http://blog.csdn.net/bornhe/article/details/8222450][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][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][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][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][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][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][http_www.springerlink.com_index_KK844421T5466K35.pdf]
 *  A Course in Machine Learning [http://ciml.info/][http_ciml.info]
 *  基于mahout构建社会化推荐引擎 [http://www.doc88.com/p-745821989892.html][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][http_blog.csdn.net_java060515_archive_2007_04_19_1570243.aspx]
 *  Design of Recommender System [http://www.slideshare.net/rashmi/design-of-recommender-systems][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][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][http_blog.csdn.net_idonot_article_details_7996733]
 *  System Architectures for Personalization and Recommendation [http://techblog.netflix.com/2013/03/system-architectures-for.html][http_techblog.netflix.com_2013_03_system-architectures-for.html]
 *  The Netflix Tech Blog [http://techblog.netflix.com/][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][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][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][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/][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][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/][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][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][http_www.jdon.com_44747]
 *  SVD 简介 [http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html][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][http_blog.csdn.net_lzt1983_article_details_7696578]
 *  关于数据挖掘学习的讨论[https://www.zhihu.com/question/20751219][https_www.zhihu.com_question_20751219]

# 论文 #

《推荐系统实战》引用

*  [P1][]
 *  [A Guide to Recommender System P4][]
 *  [Cross Selling P6][]
 *  [课程：Data Mining and E-Business: The Social Data Revolution P7)][Data Mining and E-Business_ The Social Data Revolution P7]
 *  [An Introduction to Search Engines and Web Navigation p7][]  
    　　
 *  [p8][]
 *  [p9][]
 *  [(The Youtube video recommendation system) p9][The Youtube video recommendation system_ p9]  
    　　
 *  [(PPT: Music Recommendation and Discovery) p12][PPT_ Music Recommendation and Discovery_ p12]
 *  [P13][]  
    　　
 *  [(Digg Recommendation Engine Updates) P16][Digg Recommendation Engine Updates_ P16]
 *  [(The Learning Behind Gmail Priority Inbox)p17][The Learning Behind Gmail Priority Inbox_p17]  
    　　
 *  [(Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20][Accurate is not always good_ How Accuracy Metrics have hurt Recommender Systems_ P20]
 *  [(Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23][Don_t Look Stupid_ Avoiding Pitfalls when Recommending Research Papers_P23]  
    　　
 *  [(Major componets of the gravity recommender system) P25][Major componets of the gravity recommender system_ P25]
 *  [(What is a Good Recomendation Algorithm?) P26][What is a Good Recomendation Algorithm_ P26]
 *  [(Evaluation Recommendation Systems) P27][Evaluation Recommendation Systems_ P27]  
    　　
 *  [(Music Recommendation and Discovery in the Long Tail) P29][Music Recommendation and Discovery in the Long Tail_ P29]  
    　　
 *  [(Internation Workshop on Novelty and Diversity in Recommender Systems) p29][Internation Workshop on Novelty and Diversity in Recommender Systems_ p29]
 *  [(Auralist: Introducing Serendipity into Music Recommendation ) P30][Auralist_ Introducing Serendipity into Music Recommendation _ P30]  
    　　
 *  [(Metrics for evaluating the serendipity of recommendation lists) P30][Metrics for evaluating the serendipity of recommendation lists_ P30]
 *  [(The effects of transparency on trust in and acceptance of a content-based art recommender) P31][The effects of transparency on trust in and acceptance of a content-based art recommender_ P31]  
    　　
 *  [(Trust-aware recommender systems) P31][Trust-aware recommender systems_ P31]
 *  [(Tutorial on robutness of recommender system) P32][Tutorial on robutness of recommender system_ P32]
 *  [(Five Stars Dominate Ratings) P37 ][Five Stars Dominate Ratings_ P37]
 *  [(Book-Crossing Dataset) P38 ][Book-Crossing Dataset_ P38]
 *  [(Lastfm Dataset) P39][Lastfm Dataset_ P39]  
    　　
 *  [浅谈网络世界的Power Law现象 P39][Power Law_ P39]
 *  [(MovieLens Dataset) P42][MovieLens Dataset_ P42]
 *  [(Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49][Empirical Analysis of Predictive Algorithms for Collaborative Filtering_ P49]
 *  [(Digg Vedio) P50][Digg Vedio_ P50]
 *  [(Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59][Amazon.com Recommendations Item-to-Item Collaborative Filtering_ P59]
 *  [(Greg Linden Blog) P63][Greg Linden Blog_ P63]
 *  [(One-Class Collaborative Filtering) P67][One-Class Collaborative Filtering_ P67]
 *  [(Stochastic Gradient Descent) P68 ][Stochastic Gradient Descent_ P68]
 *  [(Latent Factor Models for Web Recommender Systems) P70 ][Latent Factor Models for Web Recommender Systems_ P70]
 *  [(Bipatite Graph) P73][Bipatite Graph_ P73]
 *  [(Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74][Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation_ P74]
 *  [(Topic Sensitive Pagerank) P74][Topic Sensitive Pagerank_ P74]
 *  [(FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77][FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS_ P77]
 *  [(LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80][LIFESTYLE FINDER_ Intelligent User Profiling Using Large-Scale Demographic Data_ P80]
 *  [( adaptive bootstrapping of recommender systems using decision trees) P87][adaptive bootstrapping of recommender systems using decision trees_ P87]
 *  [(Vector Space Model) P90][Vector Space Model_ P90]
 *  [(冷启动问题的比赛) P92][P92]
 *  [(Latent Dirichlet Allocation) P92][Latent Dirichlet Allocation_ P92]
 *  [(Kullback–Leibler divergence) P93][Kullback_Leibler divergence_ P93]
 *  [(About The Music Genome Project) P94][About The Music Genome Project_ P94]
 *  [(Pandora Music Genome Project Attributes) P94][Pandora Music Genome Project Attributes_ P94]
 *  [(Jinni Movie Genome) P94][Jinni Movie Genome_ P94]
 *  [(Tagsplanations: Explaining Recommendations Using Tags) P96][Tagsplanations_ Explaining Recommendations Using Tags_ P96]
 *  [(Tag Wikipedia) P96][Tag Wikipedia_ P96]
 *  [(Nurturing Tagging Communities) P100][Nurturing Tagging Communities_ P100]
 *  [(Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100][Why We Tag_ Motivations for Annotation in Mobile and Online Media _ P100]
 *  [(Delicious Dataset) P101][Delicious Dataset_ P101]
 *  [(Finding Advertising Keywords on Web Pages) P118][Finding Advertising Keywords on Web Pages_ P118]
 *  [(基于标签的推荐系统比赛) P119][P119]
 *  [(Tag recommendations based on tensor dimensionality reduction）P119][Tag recommendations based on tensor dimensionality reduction_P119]
 *  [(latent dirichlet allocation for tag recommendation) P119][latent dirichlet allocation for tag recommendation_ P119]
 *  [(Folkrank: A ranking algorithm for folksonomies) P119][Folkrank_ A ranking algorithm for folksonomies_ P119]
 *  [(Tagommenders: Connecting Users to Items through Tags) P119][Tagommenders_ Connecting Users to Items through Tags_ P119]
 *  [(The Quest for Quality Tags) P120][The Quest for Quality Tags_ P120]
 *  [(Challenge on Context-aware Movie Recommendation) P123][Challenge on Context-aware Movie Recommendation_ P123]
 *  [(The Lifespan of a link) P125][The Lifespan of a link_ P125]
 *  [(Temporal Diversity in Recommender Systems) P129][Temporal Diversity in Recommender Systems_ P129]
 *  [(Evaluating Collaborative Filtering Over Time) P129][Evaluating Collaborative Filtering Over Time_ P129]
 *  [(Hotpot) P139 ][Hotpot_ P139]
 *  [(Google Launches Hotpot, A Recommendation Engine for Places) P139][Google Launches Hotpot_ A Recommendation Engine for Places_ P139]
 *  [(geolocated recommendations) P140][geolocated recommendations_ P140]
 *  [(A Peek Into Netflix Queues) P141][A Peek Into Netflix Queues_ P141]
 *  [(Distance Browsing in Spatial Databases1) P142][Distance Browsing in Spatial Databases1_ P142]
 *  [(Efﬁcient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143][Ef_cient Evaluation of k-Range Nearest Neighbor Queries in Road Networks_ P143]
 *  [(Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144 ][Global Advertising_ Consumers Trust Real Friends and Virtual Strangers the Most_ P144]
 *  [(Suggesting Friends Using the Implicit Social Graph) P145][Suggesting Friends Using the Implicit Social Graph_ P145]
 *  [(Friends & Frenemies: Why We Add and Remove Facebook Friends) P147][Friends _ Frenemies_ Why We Add and Remove Facebook Friends_ P147]
 *  [(Stanford Large Network Dataset Collection) P149 ][Stanford Large Network Dataset Collection_ P149]
 *  [(Workshop on Context-awareness in Retrieval and Recommendation) P151][Workshop on Context-awareness in Retrieval and Recommendation_ P151]
 *  [(Factorization vs. Regularization: Fusing Heterogeneous Social Relationships in Top-N Recommendation) P153 ][Factorization vs. Regularization_ Fusing Heterogeneous Social Relationships in Top-N Recommendation_ P153]
 *  [(Twitter, an Evolving Architecture) P154][Twitter_ an Evolving Architecture_ P154]
 *  [(Recommendations in taste related domains) P155][Recommendations in taste related domains_ P155]
 *  [(Comparing Recommendations Made by Online Systems and Friends) P155][Comparing Recommendations Made by Online Systems and Friends_ P155]
 *  [(EdgeRank: The Secret Sauce That Makes Facebook’s News Feed Tick) P157][EdgeRank_ The Secret Sauce That Makes Facebook_s News Feed Tick_ P157]
 *  [(Speak Little and Well: Recommending Conversations in Online Social Streams) P158][Speak Little and Well_ Recommending Conversations in Online Social Streams_ P158]
 *  [(Learn more about “People You May Know”) P160][Learn more about _People You May Know_ P160]
 *  [(“Make New Friends, but Keep the Old” – Recommending People on Social Networking Sites) P164 ][Make New Friends_ but Keep the Old_ _ Recommending People on Social Networking Sites_ P164]
 *  [(SoRec: Social Recommendation Using Probabilistic Matrix) P165 ][SoRec_ Social Recommendation Using Probabilistic Matrix_ P165]
 *  [(A Dynamic Bayesian Network Click Model for Web Search Ranking) P177][A Dynamic Bayesian Network Click Model for Web Search Ranking_ P177]
 *  [(Online Learning from Click Data for Sponsored Search) P177][Online Learning from Click Data for Sponsored Search_ P177]
 *  [(Contextual Advertising by Combining Relevance with Click Feedback) P177 ][Contextual Advertising by Combining Relevance with Click Feedback_ P177]
 *  [(Hulu 推荐系统架构) P178][Hulu _ P178]
 *  [(MyMedia Project) P178][MyMedia Project_ P178]
 *  [(item-based collaborative filtering recommendation algorithms) P185][item-based collaborative filtering recommendation algorithms_ P185]
 *  [(Learning Collaborative Information Filters) P186 ][Learning Collaborative Information Filters_ P186]
 *  [(Simon Funk Blog:Funk SVD) P187 ][Simon Funk Blog_Funk SVD_ P187]
 *  [(Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190 ][Factor in the Neighbors_ Scalable and Accurate Collaborative Filtering_ P190]
 *  [(Time-dependent Models in Collaborative Filtering based Recommender System) P193 ][Time-dependent Models in Collaborative Filtering based Recommender System_ P193]
 *  [(Collaborative filtering with temporal dynamics) P193][Collaborative filtering with temporal dynamics_ P193]
 *  [(Least Squares Wikipedia) P195][Least Squares Wikipedia_ P195]
 *  [(Improving regularized singular value decomposition for collaborative filtering) P195][Improving regularized singular value decomposition for collaborative filtering_ P195]
 *  [(Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model) P195][Factorization Meets the Neighborhood_ a Multifaceted Collaborative Filtering Model_ P195]

【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 #

**图书**

*  Amazon
 *  豆瓣读书
 *  当当网

**新闻**

*  Google News
 *  Genieo
 *  Getprismatic [http://getprismatic.com/][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 #

[https_github.com_Flowerowl_Big_Data_Resources]: https://github.com/Flowerowl/Big_Data_Resources
[http_pan.baidu.com_s_1EaLnZ]: http://pan.baidu.com/s/1EaLnZ
[http_www.cnblogs.com_snake-hand_archive_2013_06_10_3131145.html]: http://www.cnblogs.com/snake-hand/archive/2013/06/10/3131145.html
[http_www.thebigdata.cn_JiShuBoKe_13299.html]: http://www.thebigdata.cn/JiShuBoKe/13299.html
[http_v.youku.com_v_show_id_XNTgzNDYzMjg_.html_f_2740765]: http://v.youku.com/v_show/id_XNTgzNDYzMjg=.html?f=2740765
[http_www.tudou.com_listplay_fLDkg5e1pYM.html]: http://www.tudou.com/listplay/fLDkg5e1pYM.html
[http_pan.baidu.com_s_1koSpZ]: http://pan.baidu.com/s/1koSpZ
[http_pan.baidu.com_s_1b1xYd]: http://pan.baidu.com/s/1b1xYd
[http_v.youku.com_v_show_id_XMzAyMDYxODUy.html]: http://v.youku.com/v_show/id_XMzAyMDYxODUy.html
[http_v.163.com_special_opencourse_learningfromdata.html]: http://v.163.com/special/opencourse/learningfromdata.html
[https_www.youtube.com_watch_v_gPm8SOJ1Nao]: https://www.youtube.com/watch?v=gPm8SOJ1Nao
[https_www.youtube.com_watch_v_H2_BATE8uok]: https://www.youtube.com/watch?v=H2_BATE8uok
[https_www.youtube.com_watch_v_jA4Bh-xj_mE]: https://www.youtube.com/watch?v=jA4Bh-xj_mE
[https_www.youtube.com_watch_v_YUCsOgkFqpk]: https://www.youtube.com/watch?v=YUCsOgkFqpk
[https_www.coursera.org_learn_machine-learning]: https://www.coursera.org/learn/machine-learning
[https_www.coursera.org_course_ntumlone]: https://www.coursera.org/course/ntumlone
[http_www.kdd.org]: http://www.kdd.org
[http_icdm2015.stonybrook.edu]: http://icdm2015.stonybrook.edu/
[http_www.siam.org]: http://www.siam.org/
[http_in.sdo.com_p_1707]: http://in.sdo.com/?p=1707
[https_github.com_mendeley_mrec]: https://github.com/mendeley/mrec
[https_github.com_muricoca_crab]: https://github.com/muricoca/crab
[https_github.com_ocelma_python-recsys]: https://github.com/ocelma/python-recsys
[https_github.com_markusweimer_cofirank]: https://github.com/markusweimer/cofirank
[https_github.com_graphlab-code_graphlab]: https://github.com/graphlab-code/graphlab
[https_github.com_hernad_easyrec]: https://github.com/hernad/easyrec
[https_github.com_grouplens_lenskit]: https://github.com/grouplens/lenskit
[https_github.com_apache_mahout]: https://github.com/apache/mahout
[https_github.com_davidcelis_recommendable]: https://github.com/davidcelis/recommendable
[https_github.com_nltk_nltk]: https://github.com/nltk/nltk
[https_github.com_clips_pattern]: https://github.com/clips/pattern
[https_github.com_pydata_pyrallel]: https://github.com/pydata/pyrallel
[https_github.com_Theano_Theano]: https://github.com/Theano/Theano
[https_github.com_lisa-lab_pylearn2]: https://github.com/lisa-lab/pylearn2
[https_github.com_sloria_TextBlob]: https://github.com/sloria/TextBlob
[https_github.com_clips_MBSP]: https://github.com/clips/MBSP
[https_github.com_piskvorky_gensim]: https://github.com/piskvorky/gensim
[https_github.com_saffsd_langid.py]: https://github.com/saffsd/langid.py
[https_github.com_fxsjy_jieba]: https://github.com/fxsjy/jieba
[https_github.com_NLeSC_xtas]: https://github.com/NLeSC/xtas
[https_github.com_numpy_numpy]: https://github.com/numpy/numpy
[https_github.com_scipy_scipy]: https://github.com/scipy/scipy
[https_github.com_matplotlib_matplotlib]: https://github.com/matplotlib/matplotlib
[https_github.com_scikit-learn_scikit-learn]: https://github.com/scikit-learn/scikit-learn
[https_github.com_pydata_pandas]: https://github.com/pydata/pandas
[http_mdp-toolkit.sourceforge.net]: http://mdp-toolkit.sourceforge.net/
[https_github.com_pybrain_pybrain]: https://github.com/pybrain/pybrain
[http_pyml.sourceforge.net]: http://pyml.sourceforge.net/
[https_github.com_luispedro_milk]: https://github.com/luispedro/milk
[https_github.com_PyMVPA_PyMVPA]: https://github.com/PyMVPA/PyMVPA
[https_github.com_tensorflow_tensorflow]: https://github.com/tensorflow/tensorflow
[http_blog.sciencenet.cn_home.php_mod_space_uid_3075]: http://blog.sciencenet.cn/home.php?mod=space&uid=3075
[http_glinden.blogspot.com]: http://glinden.blogspot.com/
[http_aimotion.blogspot.com]: http://aimotion.blogspot.com/
[http_weibo.com_p_1005051686952981]: http://weibo.com/p/1005051686952981
[http_zhan.renren.com_recommendersystem]: http://zhan.renren.com/recommendersystem
[http_www.wentrue.net]: http://www.wentrue.net
[http_weibo.com_pennyliang]: http://weibo.com/pennyliang
[http_diaorui.net]: http://diaorui.net
[http_www.guwendong.com]: http://www.guwendong.com
[http_xlvector.net]: http://xlvector.net
[http_www.cnblogs.com_flclain]: http://www.cnblogs.com/flclain/
[http_blog.pluskid.org]: http://blog.pluskid.org/
[http_lovebingkuai.diandian.com]: http://lovebingkuai.diandian.com/
[http_www.cnblogs.com_LeftNotEasy]: http://www.cnblogs.com/LeftNotEasy
[http_graphlab.org_lsrs2013_program]: http://graphlab.org/lsrs2013/program/
[https_groups.google.com_forum_forum_resys]: https://groups.google.com/forum/#!forum/resys
[http_jmlr.org]: http://jmlr.org/
[http_www.52ml.net_16336.html]: http://www.52ml.net/16336.html
[http_www.thuir.cn]: http://www.thuir.cn
[http_www.52nlp.cn]: http://www.52nlp.cn/
[http_spss-market.r.blog.163.com]: http://spss-market.r.blog.163.com/
[http_blog.csdn.net_yunlong34574]: http://blog.csdn.net/yunlong34574
[http_article.yeeyan.org_view_22139_410514]: http://article.yeeyan.org/view/22139/410514
[http_www.52ml.net_16312.html]: http://www.52ml.net/16312.html
[http_www.52ml.net_16436.htm]: http://www.52ml.net/16436.htm
[http_yongfeng.me_attach_rs-survey-zhang-slices.pdf]: http://yongfeng.me/attach/rs-survey-zhang-slices.pdf
[http_pan.baidu.com_s_1bnjDBYZ]: http://pan.baidu.com/s/1bnjDBYZ
[http_blog.csdn.net_bornhe_article_details_8222450]: http://blog.csdn.net/bornhe/article/details/8222450
[http_blog.csdn.net_bornhe_article_details_8222497]: http://blog.csdn.net/bornhe/article/details/8222497
[http_www.ibm.com_developerworks_cn_web_1103_zhaoct_recommstudy1_index.html]: http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html
[http_www.tuicool.com_articles_vUvQVn]: http://www.tuicool.com/articles/vUvQVn
[http_www.slideshare.net_antiraum_recommender-engines-seminar-paper]: http://www.slideshare.net/antiraum/recommender-engines-seminar-paper
[http_dl.acm.org_citation.cfm_id_1070751]: http://dl.acm.org/citation.cfm?id=1070751
[http_www.springerlink.com_index_KK844421T5466K35.pdf]: http://www.springerlink.com/index/KK844421T5466K35.pdf
[http_ciml.info]: http://ciml.info/
[http_www.doc88.com_p-745821989892.html]: http://www.doc88.com/p-745821989892.html
[http_blog.csdn.net_java060515_archive_2007_04_19_1570243.aspx]: http://blog.csdn.net/java060515/archive/2007/04/19/1570243.aspx
[http_www.slideshare.net_rashmi_design-of-recommender-systems]: http://www.slideshare.net/rashmi/design-of-recommender-systems
[http_www.slideshare.net_blueace_how-to-build-a-recommender-system-presentation]: http://www.slideshare.net/blueace/how-to-build-a-recommender-system-presentation
[http_blog.csdn.net_idonot_article_details_7996733]: http://blog.csdn.net/idonot/article/details/7996733
[http_techblog.netflix.com_2013_03_system-architectures-for.html]: http://techblog.netflix.com/2013/03/system-architectures-for.html
[http_techblog.netflix.com]: http://techblog.netflix.com/
[http_www.infoq.com_cn_articles_baifendian-recommendation-engine]: http://www.infoq.com/cn/articles/baifendian-recommendation-engine
[http_www.infoq.com_cn_recommend]: http://www.infoq.com/cn/recommend
[http_www.infoq.com_cn_presentations_recommended-system-real-time-practice-thinking]: http://www.infoq.com/cn/presentations/recommended-system-real-time-practice-thinking
[http_www.infoq.com_cn_news_2013_10_testing-practice]: http://www.infoq.com/cn/news/2013/10/testing-practice/
[http_www.infoq.com_cn_presentations_Recommend-system-engineering]: http://www.infoq.com/cn/presentations/Recommend-system-engineering
[http_www.infoq.com_cn_articles_zyy-social-recommendation-in-renren]: http://www.infoq.com/cn/articles/zyy-social-recommendation-in-renren/
[http_www.infoq.com_cn_presentations_twenty-percent-time-to-develop-recommendation-engine]: http://www.infoq.com/cn/presentations/twenty-percent-time-to-develop-recommendation-engine
[http_www.jdon.com_44747]: http://www.jdon.com/44747
[http_www.cnblogs.com_FengYan_archive_2012_05_06_2480664.html]: http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html
[http_blog.csdn.net_lzt1983_article_details_7696578]: http://blog.csdn.net/lzt1983/article/details/7696578
[https_www.zhihu.com_question_20751219]: https://www.zhihu.com/question/20751219
[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
[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
[Power Law_ 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
[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_%28metadata%29
[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
[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
[Ef_cient 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/%24FILE/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 _ 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
[http_getprismatic.com]: http://getprismatic.com/