轉---推薦系統（資料大全） 此博文包含圖片 (2014-04-11 09:14:00)

原文來自：https://github.com/Flowerowl/Big-Data-Resources 剛在一篇個人博客中看到這篇文章，不知是否本論壇或者羣的牛人所作，因這裏面講到了推薦系統羣，故有此一說，想來作者應該不介意轉到這裏供家學習下，如有不妥，還望作者海涵。原文的開源推薦系統沒有列出SVDFeature，但在推薦系統開源軟件列表彙總和評點 http://in.sdo.com/?p=1707 這個鏈接中給出來了 原文如下： 大數據/數據挖掘/推薦系統/機器學習相關資源Share my personal resources  視頻大數據視頻以及講義http://pan.baidu.com/share/link?shareid=3860301827&uk=3978262348 浙大數據挖掘系列http://v.youku.com/v_show/id_XNTgzNDYzMjg=.html?f=2740765 用Python做科學計算http://www.tudou.com/listplay/fLDkg5e1pYM.html R語言視頻http://pan.baidu.com/s/1koSpZ Hadoop視頻http://pan.baidu.com/s/1b1xYd 42區 . 技術 . 創業 . 第二講http://v.youku.com/v_show/id_XMzAyMDYxODUy.html 加州理工學院公開課：機器學習與數據挖掘http://v.163.com/special/opencourse/learningfromdata.html 書籍各種書~各種ppt~更新中~http://pan.baidu.com/s/1EaLnZ 機器學習經典書籍小結http://www.cnblogs.com/snake-hand/archive/2013/06/10/3131145.html QQ羣機器學習&模式識別 246159753 數據挖掘機器學習 236347059 推薦系統 274750470 博客推薦系統周濤 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 推薦系統人人小站    http://zhan.renren.com/recommendersystem 阿穩  http://www.wentrue.net 梁斌  http://weibo.com/pennyliang 刁瑞  http://diaorui.net guwendong http://www.guwendong.com xlvector http://xlvector.net 懶惰啊我 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小組 https://groups.google.com/forum/#!forum/resys 機器學習Journal of Machine Learning Research http://jmlr.org/ 信息檢索清華大學信息檢索組 http://www.thuir.cn 自然語言處理我愛自然語言處理 http://www.52nlp.cn/test Github推薦系統推薦系統開源軟件列表彙總和評點 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 文章機器學習 心中永遠的正能量  http://blog.csdn.net/yunlong34574 推薦系統 Netflix 推薦系統：第一部分 http://blog.csdn.net/bornhe/article/details/8222450 Netflix 推薦系統：第二部分 http://blog.csdn.net/bornhe/article/details/8222497 探索推薦引擎內部的祕密 http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html 推薦系統resys小組線下活動見聞2009-08-22   http://www.tuicool.com/articles/vUvQVn Recommendation Engines Seminar Paper, Thomas Hess, 2009: 推薦引擎的總結性文章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/ 基於mahout構建社會化推薦引擎  http://www.doc88.com/p-745821989892.html 個性化推薦技術漫談 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 推薦系統架構小結  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/ 百分點推薦引擎——從需求到架構http://www.infoq.com/cn/articles/baifendian-recommendation-engine 推薦系統 在InfoQ上的內容  http://www.infoq.com/cn/recommend 推薦系統實時化的實踐和思考 http://www.infoq.com/cn/presentations/recommended-system-real-time-practice-thinking 質量保證的推薦實踐  http://www.infoq.com/cn/news/2013/10/testing-practice/ 推薦系統的工程挑戰  http://www.infoq.com/cn/presentations/Recommend-system-engineering 社會化推薦在人人網的應用  http://www.infoq.com/cn/articles/zyy-social-recommendation-in-renren/ 利用20%時間開發推薦引擎  http://www.infoq.com/cn/presentations/twenty-percent-time-to-develop-recommendation-engine 使用Hadoop和 Mahout實現推薦引擎 http://www.jdon.com/44747 SVD 簡介 http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html Netflix推薦系統：從評分預測到消費者法則 http://blog.csdn.net/lzt1983/article/details/7696578 《推薦系統實踐》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/  　　(課程：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/  　　（淺談網絡世界的Power Law現象） 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  　　(冷啓動問題的比賽) 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/  　　(基於標籤的推薦系統比賽) 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  　　 (Efficient 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 推薦系統架構) 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 複製代碼

沙發  發表於 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 各個領域的推薦系統 圖書     Amazon     豆瓣讀書     噹噹網 新聞     Google News     Genieo     Getprismatic http://getprismatic.com/  電影     Netflix     Jinni     MovieLens     Rotten Tomatoes     Flixster     MTime 音樂     豆瓣電臺     Lastfm     Pandora     Mufin     Lala     EMusic     Ping     蝦米電臺     Jing.FM 視頻     Youtube     Hulu     Clciker 文章     CiteULike     Google Reader     StumbleUpon 旅遊     Wanderfly     TripAdvisor 社會網絡     Facebook     Twitter 綜合     Amazon     GetGlue     Strands     Hunch