一些机器学习和推荐系统的资料

大数据/数据挖掘/推荐系统/机器学习相关资源Share my personal resources
视频大数据视频以及讲义http://pan.baidu.com/share/link?shareid=3860301827&uk=3978262348
浙大数据挖掘系列http://v.youku.com/v_show/id_XNTgzNDYzMjg=.html?f=2740765
用Python做科学计算http://www.tudou.com/listplay/fLDkg5e1pYM.html

R语言视频http://pan.baidu.com/s/1koSpZ

机器学习和深度学习资料汇总：https://github.com/ty4z2008/Qix/blob/master/dl.md

Hadoop视频http://pan.baidu.com/s/1b1xYd

中国自然语言处理http://www.nlpcn.org/

推荐系统论坛：

42区 . 技术 . 创业 . 第二讲http://v.youku.com/v_show/id_XMzAyMDYxODUy.html
加州理工学院公开课：机器学习与数据挖掘http://v.163.com/special/opencourse/learningfromdata.html
书籍各种书~各种ppt~更新中~http://pan.baidu.com/s/1EaLnZ
机器学习经典书籍小结http://www.cnblogs.com/snake-hand/archive/2013/06/10/3131145.html
QQ群机器学习&模式识别 246159753
数据挖掘机器学习 236347059
推荐系统 274750470
博客推荐系统周涛 http://blog.sciencenet.cn/home.php?mod=space&uid=3075
Greg Linden http://glinden.blogspot.com/
Marcel Caraciolo   http://aimotion.blogspot.com/
ResysChina         http://weibo.com/p/1005051686952981
推荐系统人人小站    http://zhan.renren.com/recommendersystem
阿稳  http://www.wentrue.net
梁斌  http://weibo.com/pennyliang
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
机器学习Journal of Machine Learning Research http://jmlr.org/
信息检索清华大学信息检索组 http://www.thuir.cn
自然语言处理我爱自然语言处理 http://www.52nlp.cn/test
Github推荐系统推荐系统开源软件列表汇总和评点 http://in.sdo.com/?p=1707
Mrec(Python)
https://github.com/mendeley/mrec
Crab(Python)
https://github.com/muricoca/crab
Python-recsys(Python)
https://github.com/ocelma/python-recsys
CofiRank(C++)
https://github.com/markusweimer/cofirank
GraphLab(C++)
https://github.com/graphlab-code/graphlab
EasyRec(Java)
https://github.com/hernad/easyrec
Lenskit(Java)
https://github.com/grouplens/lenskit
Mahout(Java)
https://github.com/apache/mahout
Recommendable(Ruby)
https://github.com/davidcelis/recommendable
文章机器学习
心中永远的正能量  http://blog.csdn.net/yunlong34574
推荐系统
Netflix 推荐系统：第一部分 http://blog.csdn.net/bornhe/article/details/8222450
Netflix 推荐系统：第二部分 http://blog.csdn.net/bornhe/article/details/8222497
探索推荐引擎内部的秘密 http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html
推荐系统resys小组线下活动见闻2009-08-22   http://www.tuicool.com/articles/vUvQVn
Recommendation Engines Seminar Paper, Thomas Hess, 2009: 推荐引擎的总结性文章http://www.slideshare.net/antiraum/recommender-engines-seminar-paper
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, Adomavicius, G.; Tuzhilin, A., 2005  http://dl.acm.org/citation.cfm?id=1070751
A Taxonomy of RecommenderAgents on the Internet, Montaner, M.; Lopez, B.; de la Rosa, J. L., 2003http://www.springerlink.com/index/KK844421T5466K35.pdf
A Course in Machine Learning http://ciml.info/
基于mahout构建社会化推荐引擎  http://www.doc88.com/p-745821989892.html
个性化推荐技术漫谈 http://blog.csdn.net/java060515/archive/2007/04/19/1570243.aspx
Design of Recommender System http://www.slideshare.net/rashmi/design-of-recommender-systems
How to build a recommender system http://www.slideshare.net/blueace/how-to-build-a-recommender-system-presentation
推荐系统架构小结  http://blog.csdn.net/idonot/article/details/7996733
System Architectures for Personalization and Recommendation http://techblog.netflix.com/2013/03/system-architectures-for.html
The Netflix Tech Blog http://techblog.netflix.com/
百分点推荐引擎——从需求到架构http://www.infoq.com/cn/articles/baifendian-recommendation-engine
推荐系统 在InfoQ上的内容  http://www.infoq.com/cn/recommend
推荐系统实时化的实践和思考 http://www.infoq.com/cn/presentations/recommended-system-real-time-practice-thinking
质量保证的推荐实践  http://www.infoq.com/cn/news/2013/10/testing-practice/
推荐系统的工程挑战  http://www.infoq.com/cn/presentations/Recommend-system-engineering
社会化推荐在人人网的应用  http://www.infoq.com/cn/articles/zyy-social-recommendation-in-renren/
利用20%时间开发推荐引擎  http://www.infoq.com/cn/presentations/twenty-percent-time-to-develop-recommendation-engine
使用Hadoop和 Mahout实现推荐引擎 http://www.jdon.com/44747
SVD 简介 http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html
Netflix推荐系统：从评分预测到消费者法则 http://blog.csdn.net/lzt1983/article/details/7696578
《推荐系统实践》的Reference
    http://en.wikipedia.org/wiki/Information_overload
　　 P1
　　
　　http://www.readwriteweb.com/archives/recommender_systems.php
　　(A Guide to Recommender System) P4
　　
　　
　　http://en.wikipedia.org/wiki/Cross-selling
　　 (Cross Selling) P6
　　
　　http://blog.kiwitobes.com/?p=58 ， http://stanford2009.wikispaces.com/
　　(课程：Data Mining and E-Business: The Social Data Revolution) P7
　　
　　 http://thesearchstrategy.com/ebooks/an introduction to search engines and web navigation.pdf
　　（An Introduction to Search Engines and Web Navigation） p7
　　
　　http://www.netflixprize.com/
　　p8
　　
　　http://cdn-0.nflximg.com/us/pdf/Consumer_Press_Kit.pdf
　　 p9
　　
　　 http://stuyresearch.googlecode.com/hg-history/c5aa9d65d48c787fd72dcd0ba3016938312102bd/blake/resources/p293-davidson.pdf
　　(The Youtube video recommendation system) p9
　　
　　 http://www.slideshare.net/plamere/music-recommendation-and-discovery
　　( PPT: Music Recommendation and Discovery) p12
　　
　　http://www.facebook.com/instantpersonalization/
　　P13
　　
　　 http://about.digg.com/blog/digg-recommendation-engine-updates
　　 (Digg Recommendation Engine Updates) P16
　　
　　 http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36955.pdf
　　 (The Learning Behind Gmail Priority Inbox)p17
　　
　　http://www.grouplens.org/papers/pdf/mcnee-chi06-acc.pdf
　　(Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20
　　
　　http://www-users.cs.umn.edu/~mcnee/mcnee-cscw2006.pdf
　　 (Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23
　　
　　http://www.sigkdd.org/explorations/issues/9-2-2007-12/7-Netflix-2.pdf
　　 (Major componets of the gravity recommender system) P25
　　
　　http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext
　　(What is a Good Recomendation Algorithm?) P26
　　
　　http://research.microsoft.com/pubs/115396/evaluationmetrics.tr.pdf
　　 (Evaluation Recommendation Systems) P27
　　
　　http://mtg.upf.edu/static/media/PhD_ocelma.pdf
　　(Music Recommendation and Discovery in the Long Tail) P29
　　
　　http://ir.ii.uam.es/divers2011/
　　(Internation Workshop on Novelty and Diversity in Recommender Systems) p29
　　
　　http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_21.pdf
　　(Auralist: Introducing Serendipity into Music Recommendation ) P30
　　
　　http://www.springerlink.com/content/978-3-540-78196-7/#section=239197&page=1&locus=21
　　(Metrics for evaluating the serendipity of recommendation lists) P30
　　
　　http://dare.uva.nl/document/131544
　　(The effects of transparency on trust in and acceptance of a content-based art recommender) P31
　　
　　http://brettb.net/project/papers/2007 Trust-aware recommender systems.pdf
　　 (Trust-aware recommender systems) P31
　　
　　http://recsys.acm.org/2011/pdfs/RobustTutorial.pdf
　　(Tutorial on robutness of recommender system) P32
　　
　　http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html
　　 (Five Stars Dominate Ratings) P37
　　
　　http://www.informatik.uni-freiburg.de/~cziegler/BX/
　　(Book-Crossing Dataset) P38
　　
　　http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html
　　(Lastfm Dataset) P39
　　
　　http://mmdays.com/2008/11/22/power_law_1/
　　（浅谈网络世界的Power Law现象） P39
　　
　　http://www.grouplens.org/node/73/
　　(MovieLens Dataset) P42
　　
　　http://research.microsoft.com/pubs/69656/tr-98-12.pdf
　　(Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49
　　
　　http://vimeo.com/1242909
　　(Digg Vedio) P50
　　
　　http://glaros.dtc.umn.edu/gkhome/fetch/papers/itemrsCIKM01.pdf
　　 (Evaluation of Item-Based Top-N Recommendation Algorithms) P58
　　
　　http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
　　(Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59
　　
　　http://glinden.blogspot.com/2006/03/early-amazon-similarities.html
　　 (Greg Linden Blog) P63
　　
　　http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf
　　(One-Class Collaborative Filtering) P67
　　
　　http://en.wikipedia.org/wiki/Stochastic_gradient_descent
　　(Stochastic Gradient Descent) P68
　　
　　http://www.ideal.ece.utexas.edu/seminar/LatentFactorModels.pdf
　　 (Latent Factor Models for Web Recommender Systems) P70
　　
　　http://en.wikipedia.org/wiki/Bipartite_graph
　　(Bipatite Graph) P73
　　
　　http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4072747&url=http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4072747
　　(Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74
　　
　　http://www-cs-students.stanford.edu/~taherh/papers/topic-sensitive-pagerank.pdf
　　(Topic Sensitive Pagerank) P74
　　
　　http://www.stanford.edu/dept/ICME/docs/thesis/Li-2009.pdf
　　(FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77
　　
　　https://www.aaai.org/ojs/index.php/aimagazine/article/view/1292
　　 (LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80
　　
　　http://research.yahoo.com/files/wsdm266m-golbandi.pdf
　　( adaptive bootstrapping of recommender systems using decision trees) P87
　　
　　http://en.wikipedia.org/wiki/Vector_space_model
　　(Vector Space Model) P90
　　
　　http://tunedit.org/challenge/VLNetChallenge
　　(冷启动问题的比赛) P92
　　
　　http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf
　　 (Latent Dirichlet Allocation) P92
　　
　　http://en.wikipedia.org/wiki/Kullback–Leibler_divergence
　　 (Kullback–Leibler divergence) P93
　　
　　http://www.pandora.com/about/mgp
　　(About The Music Genome Project) P94
　　
　　http://en.wikipedia.org/wiki/List_of_Music_Genome_Project_attributes
　　(Pandora Music Genome Project Attributes) P94
　　
　　http://www.jinni.com/movie-genome.html
　　(Jinni Movie Genome) P94
　　
　　http://www.shilad.com/papers/tagsplanations_iui2009.pdf
　　 (Tagsplanations: Explaining Recommendations Using Tags) P96
　　
　　http://en.wikipedia.org/wiki/Tag_(metadata)
　　(Tag Wikipedia) P96
　　
　　http://www.shilad.com/shilads_thesis.pdf
　　(Nurturing Tagging Communities) P100
　　
　　http://www.stanford.edu/~morganya/research/chi2007-tagging.pdf
　　 (Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100
　　
　　http://www.google.com/url?sa=t&rct=j&q=delicious dataset dai-larbor&source=web&cd=1&ved=0CFIQFjAA&url=http://www.dai-labor.de/en/competence_centers/irml/datasets/&ei=1R4JUKyFOKu0iQfKvazzCQ&usg=AFQjCNGuVzzKIKi3K2YFybxrCNxbtKqS4A&cad=rjt
　　(Delicious Dataset) P101
　　
　　http://research.microsoft.com/pubs/73692/yihgoca-www06.pdf
　　 (Finding Advertising Keywords on Web Pages) P118
　　
　　http://www.kde.cs.uni-kassel.de/ws/rsdc08/
　　(基于标签的推荐系统比赛) P119
　　
　　http://delab.csd.auth.gr/papers/recsys.pdf
　　（Tag recommendations based on tensor dimensionality reduction）P119
　　
　　http://www.l3s.de/web/upload/documents/1/recSys09.pdf
　　(latent dirichlet allocation for tag recommendation) P119
　　
　　http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&rep=rep1&type=pdf
　　(Folkrank: A ranking algorithm for folksonomies) P119
　　
　　http://www.grouplens.org/system/files/tagommenders_numbered.pdf
　　 (Tagommenders: Connecting Users to Items through Tags) P119
　　
　　http://www.grouplens.org/system/files/group07-sen.pdf
　　(The Quest for Quality Tags) P120
　　
　　http://2011.camrachallenge.com/
　　(Challenge on Context-aware Movie Recommendation) P123
　　
　　http://bits.blogs.nytimes.com/2011/09/07/the-lifespan-of-a-link/
　　(The Lifespan of a link) P125
　　
　　http://www0.cs.ucl.ac.uk/staff/l.capra/publications/lathia_sigir10.pdf
　　 (Temporal Diversity in Recommender Systems) P129
　　
　　http://staff.science.uva.nl/~kamps/ireval/papers/paper_14.pdf
　　 (Evaluating Collaborative Filtering Over Time) P129
　　
　　http://www.google.com/places/
　　(Hotpot) P139
　　
　　http://www.readwriteweb.com/archives/google_launches_recommendation_engine_for_places.php
　　(Google Launches Hotpot, A Recommendation Engine for Places) P139
　　
　　http://xavier.amatriain.net/pubs/GeolocatedRecommendations.pdf
　　 (geolocated recommendations) P140
　　
　　http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html
　　(A Peek Into Netflix Queues) P141
　　
　　http://www.cs.umd.edu/users/meesh/420/neighbor.pdf
　　(Distance Browsing in Spatial Databases1) P142
　　
　　http://www.eng.auburn.edu/~weishinn/papers/MDM2010.pdf
　　 (Ef?cient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143
　　
　　
　　http://blog.nielsen.com/nielsenwire/consumer/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most/
　　(Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144
　　
　　http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36371.pdf
　　(Suggesting Friends Using the Implicit Social Graph) P145
　　
　　http://blog.nielsen.com/nielsenwire/online_mobile/friends-frenemies-why-we-add-and-remove-facebook-friends/
　　(Friends & Frenemies: Why We Add and Remove Facebook Friends) P147
　　
　　http://snap.stanford.edu/data/
　　(Stanford Large Network Dataset Collection) P149
　　
　　http://www.dai-labor.de/camra2010/
　　(Workshop on Context-awareness in Retrieval and Recommendation) P151
　　
　　http://www.comp.hkbu.edu.hk/~lichen/download/p245-yuan.pdf
　　 (Factorization vs. Regularization: Fusing Heterogeneous
　　Social Relationships in Top-N Recommendation) P153
　　
　　http://www.infoq.com/news/2009/06/Twitter-Architecture/
　　(Twitter, an Evolving Architecture) P154
　　
　　http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CGQQFjAB&url=http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.3679&rep=rep1&type=pdf&ei=dIIJUMzEE8WviQf5tNjcCQ&usg=AFQjCNGw2bHXJ6MdYpksL66bhUE8krS41w&sig2=5EcEDhRe9S5SQNNojWk7_Q
　　(Recommendations in taste related domains) P155
　　
　　http://www.ercim.eu/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf
　　(Comparing Recommendations Made by Online Systems and Friends) P155
　　
　　http://techcrunch.com/2010/04/22/facebook-edgerank/
　　(EdgeRank: The Secret Sauce That Makes Facebook's News Feed Tick) P157
　　
　　http://www.grouplens.org/system/files/p217-chen.pdf
　　(Speak Little and Well: Recommending Conversations in Online Social Streams) P158
　　
　　http://blog.linkedin.com/2008/04/11/learn-more-abou-2/
　　(Learn more about “People You May Know”) P160
　　
　　http://domino.watson.ibm.com/cambridge/research.nsf/58bac2a2a6b05a1285256b30005b3953/8186a48526821924852576b300537839/$FILE/TR 2009.09 Make New Frends.pdf
　　(“Make New Friends, but Keep the Old” – Recommending People on Social Networking Sites) P164
　　
　　http://www.google.com.hk/url?sa=t&rct=j&q=social+recommendation+using+prob&source=web&cd=2&ved=0CFcQFjAB&url=http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.141.465&rep=rep1&type=pdf&ei=LY0JUJ7OL9GPiAfe8ZzyCQ&usg=AFQjCNH-xTUWrs9hkxTA8si5fztAdDAEng
　　(SoRec: Social Recommendation Using Probabilistic Matrix) P165
　　
　　http://olivier.chapelle.cc/pub/DBN_www2009.pdf
　　(A Dynamic Bayesian Network Click Model for Web Search Ranking) P177
　　
　　http://www.google.com.hk/url?sa=t&rct=j&q=online+learning+from+click+data+spnsored+search&source=web&cd=1&ved=0CFkQFjAA&url=http://www.research.yahoo.net/files/p227-ciaramita.pdf&ei=HY8JUJW8CrGuiQfpx-XyCQ&usg=AFQjCNE_CYbEs8DVo84V-0VXs5FeqaJ5GQ&cad=rjt
　　(Online Learning from Click Data for Sponsored Search) P177
　　
　　http://www.cs.cmu.edu/~deepay/mywww/papers/www08-interaction.pdf
　　(Contextual Advertising by Combining Relevance with Click Feedback) P177
　　http://tech.hulu.com/blog/2011/09/19/recommendation-system/
　　(Hulu 推荐系统架构) P178
　　
　　http://mymediaproject.codeplex.com/
　　(MyMedia Project) P178
　　
　　http://www.grouplens.org/papers/pdf/www10_sarwar.pdf
　　(item-based collaborative filtering recommendation algorithms) P185
　　
　　http://www.stanford.edu/~koutrika/Readings/res/Default/billsus98learning.pdf
　　(Learning Collaborative Information Filters) P186
　　
　　http://sifter.org/~simon/journal/20061211.html
　　(Simon Funk Blog:Funk SVD) P187
　　
　　http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf
　　(Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190
　　
　　http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh26.pdf
　　(Time-dependent Models in Collaborative Filtering based Recommender System) P193
　　
　　http://sydney.edu.au/engineering/it/~josiah/lemma/kdd-fp074-koren.pdf
　　(Collaborative filtering with temporal dynamics) P193
　　
　　http://en.wikipedia.org/wiki/Least_squares
　　(Least Squares Wikipedia) P195
　　
　　http://www.mimuw.edu.pl/~paterek/ap_kdd.pdf
　　(Improving regularized singular value decomposition for collaborative filtering) P195
　　
　　http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf
　　 (Factorization Meets the Neighborhood: a Multifaceted
　　Collaborative Filtering Model) P195
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沙发
 发表于 2014-3-19 11:59:18

【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
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