[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.readwrite web.com/archives/rec ommender_systems.php
(A Guide to Recommender System) P4
http://en.wikipedia. org/wiki/Cross-selli ng
(Cross Selling) P6
http://blog.kiwitobe s.com/?p=58 , http://stanford2009. wikispaces.com/
(课程:Data Mining and E-Business: The Social Data Revolution) P7
http://thesearchstra tegy.com/ebooks/an%2 0introduction%20to%2 0search%20engines%20 and%20web%20navigati on.pdf
(An Introduction to Search Engines and Web Navigation) p7
http://www.netflixpr ize.com/
p8
http://cdn-0.nflximg .com/us/pdf/Consumer _Press_Kit.pdf
p9
http://stuyresearch. googlecode.com/hg-hi story/c5aa9d65d48c78 7fd72dcd0ba301693831 2102bd/blake/resourc es/p293-davidson.pdf
(The Youtube video recommendation system) p9
http://www.slideshar e.net/plamere/music- recommendation-and-d iscovery
( PPT: Music Recommendation and Discovery) p12
http://www.facebook. com/instantpersonali zation/
P13
http://about.digg.co m/blog/digg-recommen dation-engine-update s
(Digg Recommendation Engine Updates) P16
http://static.google usercontent.com/exte rnal_content/untrust ed_dlcp/research.goo gle.com/en//pubs/arc hive/36955.pdf
(The Learning Behind Gmail Priority Inbox)p17
http://www.grouplens .org/papers/pdf/mcne e-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.or g/explorations/issue s/9-2-2007-12/7-Netf lix-2.pdf
(Major componets of the gravity recommender system) P25
http://cacm.acm.org/ blogs/blog-cacm/2292 5-what-is-a-good-rec ommendation-algorith m/fulltext
(What is a Good Recomendation Algorithm?) P26
http://research.micr osoft.com/pubs/11539 6/evaluationmetrics. tr.pdf
(Evaluation Recommendation Systems) P27
http://mtg.upf.edu/s tatic/media/PhD_ocel ma.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_N otes/RN_11_21.pdf
(Auralist: Introducing Serendipity into Music Recommendation ) P30
http://www.springerl ink.com/content/978- 3-540-78196-7/#secti on=239197&page=1 &locus=21
(Metrics for evaluating the serendipity of recommendation lists) P30
http://dare.uva.nl/d ocument/131544
(The effects of transparency on trust in and acceptance of a content-based art recommender) P31
http://brettb.net/pr oject/papers/2007%20 Trust-aware%20recomm ender%20systems.pdf
(Trust-aware recommender systems) P31
http://recsys.acm.or g/2011/pdfs/RobustTu torial.pdf
(Tutorial on robutness of recommender system) P32
http://youtube-globa l.blogspot.com/2009/ 09/five-stars-domina te-ratings.html
(Five Stars Dominate Ratings) P37
http://www.informati k.uni-freiburg.de/~c ziegler/BX/
(Book-Crossing Dataset) P38
http://www.dtic.upf. edu/~ocelma/MusicRec ommendationDataset/l astfm-1K.html
(Lastfm Dataset) P39
http://mmdays.com/20 08/11/22/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.micr osoft.com/pubs/69656 /tr-98-12.pdf
(Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49
http://vimeo.com/124 2909
(Digg Vedio) P50
http://glaros.dtc.um n.edu/gkhome/fetch/p apers/itemrsCIKM01.p df
(Evaluation of Item-Based Top-N Recommendation Algorithms) P58
http://www.cs.umd.ed u/~samir/498/Amazon- Recommendations.pdf
(Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59
http://glinden.blogs pot.com/2006/03/earl y-amazon-similaritie s.html
(Greg Linden Blog) P63
http://www.hpl.hp.co m/techreports/2008/H PL-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.p df
(Latent Factor Models for Web Recommender Systems) P70
http://en.wikipedia. org/wiki/Bipartite_g raph
(Bipatite Graph) P73
http://ieeexplore.ie ee.org/xpl/login.jsp ?tp=&arnumber=40 72747&url=http%3 A%2F%2Fieeexplore.ie ee.org%2Fxpls%2Fabs_ all.jsp%3Farnumber%3 D4072747
(Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74
http://www-cs-studen ts.stanford.edu/~tah erh/papers/topic-sen sitive-pagerank.pdf
(Topic Sensitive Pagerank) P74
http://www.stanford. edu/dept/ICME/docs/t hesis/Li-2009.pdf
(FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77
https://www.aaai.org /ojs/index.php/aimag azine/article/view/1 292
(LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80
http://research.yaho o.com/files/wsdm266m -golbandi.pdf
( adaptive bootstrapping of recommender systems using decision trees) P87
http://en.wikipedia. org/wiki/Vector_spac e_model
(Vector Space Model) P90
http://tunedit.org/c hallenge/VLNetChalle nge
(冷启动问题的比赛) P92
http://www.cs.prince ton.edu/~blei/papers /BleiNgJordan2003.pd f
(Latent Dirichlet Allocation) P92
http://en.wikipedia. org/wiki/Kullback%E2 %80%93Leibler_diverg ence
(Kullback–Leibler divergence) P93
http://www.pandora.c om/about/mgp
(About The Music Genome Project) P94
http://en.wikipedia. org/wiki/List_of_Mus ic_Genome_Project_at tributes
(Pandora Music Genome Project Attributes) P94
http://www.jinni.com /movie-genome.html
(Jinni Movie Genome) P94
http://www.shilad.co m/papers/tagsplanati ons_iui2009.pdf
(Tagsplanations: Explaining Recommendations Using Tags) P96
http://en.wikipedia. org/wiki/Tag_(metada ta)
(Tag Wikipedia) P96
http://www.shilad.co m/shilads_thesis.pdf
(Nurturing Tagging Communities) P100
http://www.stanford. edu/~morganya/resear ch/chi2007-tagging.p df
(Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100
http://www.google.co m/url?sa=t&rct=j &q=delicious%20d ataset%20dai-larbor& amp;source=web&c d=1&ved=0CFIQFjA A&url=http%3A%2F %2Fwww.dai-labor.de% 2Fen%2Fcompetence_ce nters%2Firml%2Fdatas ets%2F&ei=1R4JUK yFOKu0iQfKvazzCQ& ;usg=AFQjCNGuVzzKIKi 3K2YFybxrCNxbtKqS4A& amp;cad=rjt
(Delicious Dataset) P101
http://research.micr osoft.com/pubs/73692 /yihgoca-www06.pdf
(Finding Advertising Keywords on Web Pages) P118
http://www.kde.cs.un i-kassel.de/ws/rsdc0 8/
(基于标签的推荐系统比赛) P119
http://delab.csd.aut h.gr/papers/recsys.p df
(Tag recommendations based on tensor dimensionality reduction)P119
http://www.l3s.de/we b/upload/documents/1 /recSys09.pdf
(latent dirichlet allocation for tag recommendation) P119
http://citeseerx.ist .psu.edu/viewdoc/dow nload?doi=10.1.1.94. 5271&rep=rep1&am p;type=pdf
(Folkrank: A ranking algorithm for folksonomies) P119
http://www.grouplens .org/system/files/ta gommenders_numbered. pdf
(Tagommenders: Connecting Users to Items through Tags) P119
http://www.grouplens .org/system/files/gr oup07-sen.pdf
(The Quest for Quality Tags) P120
http://2011.camracha llenge.com/
(Challenge on Context-aware Movie Recommendation) P123
http://bits.blogs.ny times.com/2011/09/07 /the-lifespan-of-a-l ink/
(The Lifespan of a link) P125
http://www0.cs.ucl.a c.uk/staff/l.capra/p ublications/lathia_s igir10.pdf
(Temporal Diversity in Recommender Systems) P129
http://staff.science .uva.nl/~kamps/ireva l/papers/paper_14.pd f
(Evaluating Collaborative Filtering Over Time) P129
http://www.google.co m/places/
(Hotpot) P139
http://www.readwrite web.com/archives/goo gle_launches_recomme ndation_engine_for_p laces.php
(Google Launches Hotpot, A Recommendation Engine for Places) P139
http://xavier.amatri ain.net/pubs/Geoloca tedRecommendations.p df
(geolocated recommendations) P140
http://www.nytimes.c om/interactive/2010/ 01/10/nyregion/20100 110-netflix-map.html
(A Peek Into Netflix Queues) P141
http://www.cs.umd.ed u/users/meesh/420/ne ighbor.pdf
(Distance Browsing in Spatial Databases1) P142
http://www.eng.aubur n.edu/~weishinn/pape rs/MDM2010.pdf
(Efficient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143
http://blog.nielsen. com/nielsenwire/cons umer/global-advertis ing-consumers-trust- real-friends-and-vir tual-strangers-the-m ost/
(Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144
http://static.google usercontent.com/exte rnal_content/untrust ed_dlcp/research.goo gle.com/en//pubs/arc hive/36371.pdf
(Suggesting Friends Using the Implicit Social Graph) P145
http://blog.nielsen. com/nielsenwire/onli ne_mobile/friends-fr enemies-why-we-add-a nd-remove-facebook-f riends/
(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/down load/p245-yuan.pdf
(Factorization vs. Regularization: Fusing Heterogeneous
Social Relationships in Top-N Recommendation) P153
http://www.infoq.com /news/2009/06/Twitte r-Architecture/
(Twitter, an Evolving Architecture) P154
http://www.google.co m/url?sa=t&rct=j &q=&esrc=s&a mp;source=web&cd =2&ved=0CGQQFjAB &url=http%3A%2F% 2Fciteseerx.ist.psu. edu%2Fviewdoc%2Fdown load%3Fdoi%3D10.1.1. 165.3679%26rep%3Drep 1%26type%3Dpdf&e i=dIIJUMzEE8WviQf5tN jcCQ&usg=AFQjCNG w2bHXJ6MdYpksL66bhUE 8krS41w&sig2=5Ec EDhRe9S5SQNNojWk7_Q
(Recommendations in taste related domains) P155
http://www.ercim.eu/ publication/ws-proce edings/DelNoe02/Rash miSinha.pdf
(Comparing Recommendations Made by Online Systems and Friends) P155
http://techcrunch.co m/2010/04/22/faceboo k-edgerank/
(EdgeRank: The Secret Sauce That Makes Facebook's News Feed Tick) P157
http://www.grouplens .org/system/files/p2 17-chen.pdf
(Speak Little and Well: Recommending Conversations in Online Social Streams) P158
http://blog.linkedin .com/2008/04/11/lear n-more-abou-2/
(Learn more about “People You May Know”) P160
http://domino.watson .ibm.com/cambridge/r esearch.nsf/58bac2a2 a6b05a1285256b30005b 3953/8186a4852682192 4852576b300537839/$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.co m.hk/url?sa=t&rc t=j&q=social+rec ommendation+using+pr ob&source=web&am p;cd=2&ved=0CFcQ FjAB&url=http%3A %2F%2Fciteseerx.ist. psu.edu%2Fviewdoc%2F download%3Fdoi%3D10. 1.1.141.465%26rep%3D rep1%26type%3Dpdf&am p;ei=LY0JUJ7OL9GPiAf e8ZzyCQ&usg=AFQj CNH-xTUWrs9hkxTA8si5 fztAdDAEng
(SoRec: Social Recommendation Using Probabilistic Matrix) P165
http://olivier.chape lle.cc/pub/DBN_www20 09.pdf
(A Dynamic Bayesian Network Click Model for Web Search Ranking) P177
http://www.google.co m.hk/url?sa=t&rc t=j&q=online+lea rning+from+click+dat a+spnsored+search&am p;source=web&cd= 1&ved=0CFkQFjAA& amp;url=http%3A%2F%2 Fwww.research.yahoo. net%2Ffiles%2Fp227-c iaramita.pdf&ei= HY8JUJW8CrGuiQfpx-Xy CQ&usg=AFQjCNE_C YbEs8DVo84V-0VXs5Feq aJ5GQ&cad=rjt
(Online Learning from Click Data for Sponsored Search) P177
http://www.cs.cmu.ed u/~deepay/mywww/pape rs/www08-interaction .pdf
(Contextual Advertising by Combining Relevance with Click Feedback) P177
http://tech.hulu.com /blog/2011/09/19/rec ommendation-system/
(Hulu 推荐系统架构) P178
http://mymediaprojec t.codeplex.com/
(MyMedia Project) P178
http://www.grouplens .org/papers/pdf/www1 0_sarwar.pdf
(item-based collaborative filtering recommendation algorithms) P185
http://www.stanford. edu/~koutrika/Readin gs/res/Default/bills us98learning.pdf
(Learning Collaborative Information Filters) P186
http://sifter.org/~s imon/journal/2006121 1.html
(Simon Funk Blog:Funk SVD) P187
http://courses.ischo ol.berkeley.edu/i290 -dm/s11/SECURE/a1-ko ren.pdf
(Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190
http://nlpr-web.ia.a c.cn/2009papers/gjhy /gh26.pdf
(Time-dependent Models in Collaborative Filtering based Recommender System) P193
http://sydney.edu.au /engineering/it/~jos iah/lemma/kdd-fp074- koren.pdf
(Collaborative filtering with temporal dynamics) P193
http://en.wikipedia. org/wiki/Least_squar es
(Least Squares Wikipedia) P195
http://www.mimuw.edu .pl/~paterek/ap_kdd. pdf
(Improving regularized singular value decomposition for collaborative filtering) P195
http://public.resear ch.att.com/~volinsky /netflix/kdd08koren. pdf
(Factorization Meets the Neighborhood: a Multifaceted
Collaborative Filtering Model) P195
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