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
- 机器学习&模式识别 246159753
- 数据挖掘机器学习 236347059
- 推荐系统 274750470
- 36大数据 80958753
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://graphlab.org/lsrs2013/program/
- 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/
- 36大数据 http://www.36dsj.com/
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 http://article.yeeyan.org/view/22139/410514
- Books for Machine Learning with R http://www.52ml.net/16312.html
- 是什么阻碍了你的机器学习目标？ http://www.52ml.net/16436.htm
- 推荐系统初探 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., 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
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)
- An Introduction to Search Engines and Web Navigation p7
- p8
- p9
- (The Youtube video recommendation system) p9
- (PPT: Music Recommendation and Discovery) p12
- P13
- (Digg Recommendation Engine Updates) P16
- (The Learning Behind Gmail Priority Inbox)p17
- (Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20
- (Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23
- (Major componets of the gravity recommender system) P25
- (What is a Good Recomendation Algorithm?) P26
- (Evaluation Recommendation Systems) P27
- (Music Recommendation and Discovery in the Long Tail) P29
- (Internation Workshop on Novelty and Diversity in Recommender Systems) p29
- (Auralist: Introducing Serendipity into Music Recommendation ) 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
- (Trust-aware recommender systems) P31
- (Tutorial on robutness of recommender system) P32
- (Five Stars Dominate Ratings) P37
- (Book-Crossing Dataset) P38
- (Lastfm Dataset) P39
- 浅谈网络世界的Power Law现象 P39
- (MovieLens Dataset) P42
- (Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49
- (Digg Vedio) P50
- (Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59
- (Greg Linden Blog) P63
- (One-Class Collaborative Filtering) P67
- (Stochastic Gradient Descent) P68
- (Latent Factor Models for Web Recommender Systems) P70
- (Bipatite Graph) P73
- (Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74
- (Topic Sensitive Pagerank) P74
- (FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77
- (LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80
- ( adaptive bootstrapping of recommender systems using decision trees) P87
- (Vector Space Model) P90
- (冷启动问题的比赛) P92
- (Latent Dirichlet Allocation) P92
- (Kullback–Leibler divergence) P93
- (About The Music Genome Project) P94
- (Pandora Music Genome Project Attributes) P94
- (Jinni Movie Genome) P94
- (Tagsplanations: Explaining Recommendations Using Tags) P96
- (Tag Wikipedia) P96
- (Nurturing Tagging Communities) P100
- (Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100
- (Delicious Dataset) P101
- (Finding Advertising Keywords on Web Pages) P118
- (基于标签的推荐系统比赛) P119
- (Tag recommendations based on tensor dimensionality reduction）P119
- (latent dirichlet allocation for tag recommendation) P119
- (Folkrank: A ranking algorithm for folksonomies) P119
- (Tagommenders: Connecting Users to Items through Tags) P119
- (The Quest for Quality Tags) P120
- (Challenge on Context-aware Movie Recommendation) P123
- (The Lifespan of a link) P125
- (Temporal Diversity in Recommender Systems) P129
- (Evaluating Collaborative Filtering Over Time) P129
- (Hotpot) P139
- (Google Launches Hotpot, A Recommendation Engine for Places) P139
- (geolocated recommendations) P140
- (A Peek Into Netflix Queues) P141
- (Distance Browsing in Spatial Databases1) P142
- (Efficient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143
- (Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144
- (Suggesting Friends Using the Implicit Social Graph) P145
- (Friends & Frenemies: Why We Add and Remove Facebook Friends) P147
- (Stanford Large Network Dataset Collection) P149
- (Workshop on Context-awareness in Retrieval and Recommendation) P151
- (Factorization vs. Regularization: Fusing Heterogeneous Social Relationships in Top-N Recommendation) P153
- (Twitter, an Evolving Architecture) P154
- (Recommendations in taste related domains) P155
- (Comparing Recommendations Made by Online Systems and Friends) P155
- (EdgeRank: The Secret Sauce That Makes Facebook’s News Feed Tick) P157
- (Speak Little and Well: Recommending Conversations in Online Social Streams) P158
- (Learn more about “People You May Know”) P160
- (“Make New Friends, but Keep the Old” – Recommending People on Social Networking Sites) P164
- (SoRec: Social Recommendation Using Probabilistic Matrix) P165
- (A Dynamic Bayesian Network Click Model for Web Search Ranking) P177
- (Online Learning from Click Data for Sponsored Search) P177
- (Contextual Advertising by Combining Relevance with Click Feedback) P177
- (Hulu 推荐系统架构) P178
- (MyMedia Project) P178
- (item-based collaborative filtering recommendation algorithms) P185
- (Learning Collaborative Information Filters) P186
- (Simon Funk Blog:Funk SVD) P187
- (Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190
- (Time-dependent Models in Collaborative Filtering based Recommender System) P193
- (Collaborative filtering with temporal dynamics) P193
- (Least Squares Wikipedia) P195
- (Improving regularized singular value decomposition for collaborative filtering) 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
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
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数据挖掘不仅仅是简单的数据分析，它包括了机器学习、算法学习和专家系统等多个方面，致力于从含噪声的真实数据中发现新颖且实用的知识。数据挖掘的定义强调了其核心特点：数据源的复杂性，发现的知识应与用户需求...
数据挖掘技术通过使用统计学、机器学习、数据库系统以及其他领域的算法来分析数据，预测趋势和行为模式，从而在多个行业中发挥关键作用。 数据挖掘技术的含义及作用是这篇文章的切入点。数据挖掘的含义通常指利用...
"人工智能论文-机器学习与大数据.pdf" 在这篇论文中，我们可以...机器学习可以解决大数据处理的挑战，大数据也为机器学习提供了大量的数据资源。我们需要进一步地研究和发展机器学习技术，以满足大数据时代的需求。
本项目基于Python语言构建，利用大数据处理技术和机器学习算法，实现了一套高效且精准的推荐系统。下面将详细阐述相关知识点。 一、Python编程语言 Python是一种高级编程语言，以其简洁易读的语法和丰富的库资源...
在大数据分析中，数据挖掘通过运用机器学习算法、统计方法和模式识别等手段，帮助我们理解数据背后的规律，从而支持决策制定和预测未来趋势。 大数据行业应用广泛，包括但不限于电子商务、社交网络、智能交通、医疗...
数据挖掘是利用统计和机器学习技术从大量数据中发现模式的过程。在本课程设计中，数据预处理是关键步骤，包括描述性数据汇总，即对数据进行统计分析，如计算平均值、中位数、频率分布等，以了解数据的基本特征。决策...