附上Reference 中的干货 （Paper,Blog等资料的链接）

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