大数据/数据挖掘/推荐系统/机器学习相关资源
4,313 次阅读 - 资源书籍
- 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
- 机器学习&深度学习经典资料汇总 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
- 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
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
- 机器学习群：463848712
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
库
- NLTK https://github.com/nltk/nltk
- Pattern https://github.com/clips/pattern
- Pyrallel https://github.com/pydata/pyrallel
- Theano https://github.com/Theano/Theano
- Pylearn2 https://github.com/lisa-lab/pylearn2
- TextBlob https://github.com/sloria/TextBlob
- MBSP https://github.com/clips/MBSP
- Gensim https://github.com/piskvorky/gensim
- Langid.py https://github.com/saffsd/langid.py
- Jieba https://github.com/fxsjy/jieba
- xTAS https://github.com/NLeSC/xtas
- NumPy https://github.com/numpy/numpy
- SciPy https://github.com/scipy/scipy
- Matplotlib https://github.com/matplotlib/matplotlib
- scikit-learn https://github.com/scikit-learn/scikit-learn
- Pandas https://github.com/pydata/pandas
- MDP http://mdp-toolkit.sourceforge.net/
- PyBrain https://github.com/pybrain/pybrain
- PyML http://pyml.sourceforge.net/
- Milk https://github.com/luispedro/milk
- PyMVPA https://github.com/PyMVPA/PyMVPA
博客
- 周涛 http://blog.sciencenet.cn/home.php?mod=space&uid=3075
- Greg Linden http://glinden.blogspot.com/
- Marcel Caraciolo http://aimotion.blogspot.com/
- RsysChina 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://www.kuqin.com/shuoit/20151130/349205.html
- Google小组 https://groups.google.com/forum/#!forum/resys
- Journal of Machine Learning Research http://jmlr.org/
- 在线的机器学习社区 http://www.52ml.net/16336.html
- 清华大学信息检索组 http://www.thuir.cn
- 我爱自然语言处理 http://www.52nlp.cn/
- 数盟社区：http://dataunion.org/
文章
- 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 《机器学习最佳入门学习资料汇总》
- 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 Recommendationhttp://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《百分点推荐引擎——从需求到架构》
- 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
论文
- (Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model) P195
- (Improving regularized singular value decomposition for collaborative filtering) P195
- (Least Squares Wikipedia) P195
- (Collaborative filtering with temporal dynamics) P193
- (Time-dependent Models in Collaborative Filtering based Recommender System) P193
- (Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190
- (Simon Funk Blog:Funk SVD) P187
- (Learning Collaborative Information Filters) P186
- (item-based collaborative filtering recommendation algorithms) P185
- (MyMedia Project) P178
- (Hulu 推荐系统架构) P178
- (Contextual Advertising by Combining Relevance with Click Feedback) P177
- (Online Learning from Click Data for Sponsored Search) P177
- (A Dynamic Bayesian Network Click Model for Web Search Ranking) P177
- (SoRec: Social Recommendation Using Probabilistic Matrix) P165
- (“Make New Friends, but Keep the Old” – Recommending People on Social Networking Sites) P164
- (Learn more about “People You May Know”) P160
- (Speak Little and Well: Recommending Conversations in Online Social Streams) P158
- (EdgeRank: The Secret Sauce That Makes Facebook’s News Feed Tick) P157
- (Comparing Recommendations Made by Online Systems and Friends) P155
- (Recommendations in taste related domains) P155
- (Twitter, an Evolving Architecture) P154
- (Factorization vs. Regularization: Fusing Heterogeneous Social Relationships in Top-N Recommendation) P153
- (Workshop on Context-awareness in Retrieval and Recommendation) P151
- (Stanford Large Network Dataset Collection) P149
- (Friends & Frenemies: Why We Add and Remove Facebook Friends) P147
- (Suggesting Friends Using the Implicit Social Graph) P145
- (Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144
- (Efficient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143
- (Distance Browsing in Spatial Databases1) P142
- (A Peek Into Netflix Queues) P141
- (geolocated recommendations) P140
- (Google Launches Hotpot, A Recommendation Engine for Places) P139
- (Hotpot) P139
- (Evaluating Collaborative Filtering Over Time) P129
- (Temporal Diversity in Recommender Systems) P129
- (The Lifespan of a link) P125
- (Challenge on Context-aware Movie Recommendation) P123
- (The Quest for Quality Tags) P120
- (Tagommenders: Connecting Users to Items through Tags) P119
- (Folkrank: A ranking algorithm for folksonomies) P119
- (latent dirichlet allocation for tag recommendation) P119
- (Tag recommendations based on tensor dimensionality reduction）P119
- (基于标签的推荐系统比赛) P119
- (Finding Advertising Keywords on Web Pages) P118
- (Delicious Dataset) P101
- (Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100
- (Nurturing Tagging Communities) P100
- (Tag Wikipedia) P96
- (Tagsplanations: Explaining Recommendations Using Tags) P96
- (Jinni Movie Genome) P94
- (Pandora Music Genome Project Attributes) P94
- (About The Music Genome Project) P94
- (Kullback–Leibler divergence) P93
- (Latent Dirichlet Allocation) P92
- (冷启动问题的比赛) P92
- (Vector Space Model) P90
- ( adaptive bootstrapping of recommender systems using decision trees) P87
- (LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80
- (FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77
- (Topic Sensitive Pagerank) P74
- (Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74
- (Bipatite Graph) P73
- (Latent Factor Models for Web Recommender Systems) P70
- (Stochastic Gradient Descent) P68
- (One-Class Collaborative Filtering) P67
- (Greg Linden Blog) P63
- (Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59
- (Digg Vedio) P50
- (Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49
- (MovieLens Dataset) P42
- 浅谈网络世界的Power Law现象 P39
- (Lastfm Dataset) P39
- (Book-Crossing Dataset) P38
- (Five Stars Dominate Ratings) P37
- (Tutorial on robutness of recommender system) P32
- (Trust-aware recommender systems) P31
- (The effects of transparency on trust in and acceptance of a content-based art recommender) P31
- (Metrics for evaluating the serendipity of recommendation lists) P30
- (Auralist: Introducing Serendipity into Music Recommendation ) P30
- (Internation Workshop on Novelty and Diversity in Recommender Systems) p29
- (Music Recommendation and Discovery in the Long Tail) P29
- (Evaluation Recommendation Systems) P27
- (What is a Good Recomendation Algorithm?) P26
- (Major componets of the gravity recommender system) P25
- (Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23
- (Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20
- (The Learning Behind Gmail Priority Inbox)p17
- (Digg Recommendation Engine Updates) P16
- http://www.facebook.com/instantpersonalization/ P13
- (PPT: Music Recommendation and Discovery) p12
- (The Youtube video recommendation system) p9
- http://cdn-0.nflximg.com/us/pdf/Consumer_Press_Kit.pdf p9
- http://www.netflixprize.com/ p8
- An Introduction to Search Engines and Web Navigation p7
- 课程：Data Mining and E-Business: The Social Data Revolution P7)
- Cross Selling P6
- A Guide to Recommender System P4
- http://www.kuqin.com/shuoit/20151130/349205.html P1
- 【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/
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
综合
- Amazon
- GetGlue
- Strands
- Hunch
来源：酷勤网
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