[推荐系统]推荐系统实践Reference

　这只是一本197页的书 

　　 

　　我想你未必过瘾 

　　但作者附上了诸多好资料 

　　无论是paper, blog文章,wikipedia词条，数据集还是开源项目等 

　　你可以选择拥有 

　　附上我收集的资料链接，格式基本按照‘url+资料名称+出现在书中的页数’，某些链接可能需要你翻过一道‘墙’,某些重复引用的我就没重复贴上链接了 

　　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%20introduction%20to%20search%20engines%20and%20web%20navigation.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%20trust-aware%20recommender%20systems.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%3a%2f%2fieeexplore.ieee.org%2fxpls%2fabs_all.jsp%3farnumber%3d4072747 

　　(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%e2%80%93leibler_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%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 

　　(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 

　　 (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%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 

　　(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%202009.09%20make%20new%20frends.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%3a%2f%2fciteseerx.ist.psu.edu%2fviewdoc%2fdownload%3fdoi%3d10.1.1.141.465%26rep%3drep1%26type%3dpdf&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%3a%2f%2fwww.research.yahoo.net%2ffiles%2fp227-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