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https://github.com/weiweifan/Big-Data-Resources
大数据/数据挖掘/推荐系统/机器学习相关资源
https://github.com/weiweifan/Big-Data-Resources
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大数据/数据挖掘/推荐系统/机器学习相关资源
- Host: GitHub
- URL: https://github.com/weiweifan/Big-Data-Resources
- Owner: weiweifan
- Created: 2014-03-16T14:04:06.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2019-10-14T03:37:14.000Z (about 5 years ago)
- Last Synced: 2024-10-31T11:06:14.892Z (14 days ago)
- Homepage:
- Size: 297 KB
- Stars: 1,371
- Watchers: 71
- Forks: 972
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
#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
###浙大数据挖掘系列
###用Python做科学计算
###R语言视频
###Hadoop视频
###42区 . 技术 . 创业 . 第二讲
###加州理工学院公开课：机器学习与数据挖掘
=======================
##书籍
###各种书~各种ppt~更新中~
###机器学习经典书籍小结
=======================
##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
Greg Linden
Marcel Caraciolo
ResysChina
推荐系统人人小站
阿稳
梁斌
刁瑞
guwendong
xlvector
懒惰啊我
free mind
lovebingkuai
LeftNotEasy
LSRS 2013
Google小组
###机器学习
Journal of Machine Learning Research
###信息检索
清华大学信息检索组
###自然语言处理
我爱自然语言处理
test
##Github
###推荐系统
推荐系统开源软件列表汇总和评点
Mrec(Python)
Crab(Python)
Python-recsys(Python)
CofiRank(C++)
GraphLab(C++)
EasyRec(Java)
Lenskit(Java)
Mahout(Java)
Recommendable(Ruby)
##文章
###机器学习
* 心中永远的正能量
###推荐系统
* Netflix 推荐系统：第一部分
* Netflix 推荐系统：第二部分
* 探索推荐引擎内部的秘密
* 推荐系统resys小组线下活动见闻2009-08-22
* Recommendation Engines Seminar Paper, Thomas Hess, 2009: 推荐引擎的总结性文章
* Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, Adomavicius, G.; Tuzhilin, A., 2005
* A Taxonomy of RecommenderAgents on the Internet, Montaner, M.; Lopez, B.; de la Rosa, J. L., 2003
* A Course in Machine Learning
* 基于mahout构建社会化推荐引擎
* 个性化推荐技术漫谈
* Design of Recommender System
* How to build a recommender system
* 推荐系统架构小结
* System Architectures for Personalization and Recommendation
* The Netflix Tech Blog
* 百分点推荐引擎——从需求到架构
* 推荐系统 在InfoQ上的内容
* 推荐系统实时化的实践和思考
* 质量保证的推荐实践
* 推荐系统的工程挑战
* 社会化推荐在人人网的应用
* 利用20%时间开发推荐引擎
* 使用Hadoop和 Mahout实现推荐引擎
* SVD 简介
* Netflix推荐系统：从评分预测到消费者法则
* 《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%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
【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
##各个领域的推荐系统
**图书**
* Amazon
* 豆瓣读书
* 当当网
**新闻**
* Google News
* Genieo
* Getprismatic
**电影**
* Netflix
* Jinni
* MovieLens
* Rotten Tomatoes
* Flixster
* MTime
**音乐**
* 豆瓣电台
* Lastfm
* Pandora
* Mufin
* Lala
* EMusic
* Ping
* 虾米电台
* Jing.FM
**视频**
* Youtube
* Hulu
* Clciker
**文章**
* CiteULike
* Google Reader
* StumbleUpon
**旅游**
* Wanderfly
* TripAdvisor
**社会网络**
* Facebook
* Twitter
**综合**
* Amazon
* GetGlue
* Strands
* Hunch
##欢迎贡献资源~~待续