推荐系统资料汇总

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http://pan.baidu.com/share/link?shareid=3860301827&uk=3978262348
浙大数据挖掘系列http://v.youku.com/v_show/id_XNTgzNDYzMjg=.html?f=2740765
用Python做科学计算http://www.tudou.com/listplay/fLDkg5e1pYM.html
R语言视频http://pan.baidu.com/s/1koSpZ
Hadoop视频http://pan.baidu.com/s/1b1xYd
42区 . 技术 . 创业 . 第二讲http://v.youku.com/v_show/id_XMzAyMDYxODUy.html
加州理工学院公开课:机器学习与数据挖掘http://v.163.com/special/opencourse/learningfromdata.html
书籍各种书~各种ppt~更新中~http://pan.baidu.com/s/1EaLnZ
机器学习经典书籍小结http://www.cnblogs.com/snake-hand/archive/2013/06/10/3131145.html
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 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
推荐系统人人小站    http://zhan.renren.com/recommendersystem
阿稳  http://www.wentrue.net
梁斌  http://weibo.com/pennyliang
刁瑞  http://diaorui.net
guwendong http://www.guwendong.com
xlvector http://xlvector.net
懒惰啊我 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小组 https://groups.google.com/forum/#!forum/resys
机器学习Journal of Machine Learning Research http://jmlr.org/
信息检索清华大学信息检索组 http://www.thuir.cn
自然语言处理我爱自然语言处理 http://www.52nlp.cn/test
Github推荐系统推荐系统开源软件列表汇总和评点 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
文章机器学习

推荐系统

  • Netflix 推荐系统:第一部分 http://blog.csdn.net/bornhe/article/details/8222450
  • Netflix 推荐系统:第二部分 http://blog.csdn.net/bornhe/article/details/8222497
  • 探索推荐引擎内部的秘密 http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html
  • 推荐系统resys小组线下活动见闻2009-08-22   http://www.tuicool.com/articles/vUvQVn
  • Recommendation Engines Seminar Paper, Thomas Hess, 2009: 推荐引擎的总结性文章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/
  • 基于mahout构建社会化推荐引擎  http://www.doc88.com/p-745821989892.html
  • 个性化推荐技术漫谈 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
  • 推荐系统架构小结  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/
  • 百分点推荐引擎——从需求到架构http://www.infoq.com/cn/articles/baifendian-recommendation-engine
  • 推荐系统 在InfoQ上的内容  http://www.infoq.com/cn/recommend
  • 推荐系统实时化的实践和思考 http://www.infoq.com/cn/presentations/recommended-system-real-time-practice-thinking
  • 质量保证的推荐实践  http://www.infoq.com/cn/news/2013/10/testing-practice/
  • 推荐系统的工程挑战  http://www.infoq.com/cn/presentations/Recommend-system-engineering
  • 社会化推荐在人人网的应用  http://www.infoq.com/cn/articles/zyy-social-recommendation-in-renren/
  • 利用20%时间开发推荐引擎  http://www.infoq.com/cn/presentations/twenty-percent-time-to-develop-recommendation-engine
  • 使用Hadoop和 Mahout实现推荐引擎 http://www.jdon.com/44747
  • SVD 简介 http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html
  • Netflix推荐系统:从评分预测到消费者法则 http://blog.csdn.net/lzt1983/article/details/7696578
  • 《推荐系统实践》的Reference
    1. http://en.wikipedia.org/wiki/Information_overload
    2.    P1
    3.   
    4.   http://www.readwriteweb.com/archives/recommender_systems.php
    5.   (A Guide to Recommender System) P4
    6.   
    7.   
    8.   http://en.wikipedia.org/wiki/Cross-selling
    9.    (Cross Selling) P6
    10.   
    11.   http://blog.kiwitobes.com/?p=58 , http://stanford2009.wikispaces.com/
    12.   (课程:Data Mining and E-Business: The Social Data Revolution) P7
    13.   
    14.    http://thesearchstrategy.com/ebooks/an introduction to search engines and web navigation.pdf
    15.   (An Introduction to Search Engines and Web Navigation) p7
    16.   
    17.   http://www.netflixprize.com/
    18.   p8
    19.   
    20.   http://cdn-0.nflximg.com/us/pdf/Consumer_Press_Kit.pdf
    21.    p9
    22.   
    23.    http://stuyresearch.googlecode.com/hg-history/c5aa9d65d48c787fd72dcd0ba3016938312102bd/blake/resources/p293-davidson.pdf
    24.   (The Youtube video recommendation system) p9
    25.   
    26.    http://www.slideshare.net/plamere/music-recommendation-and-discovery
    27.   ( PPT: Music Recommendation and Discovery) p12
    28.   
    29.   http://www.facebook.com/instantpersonalization/
    30.   P13
    31.   
    32.    http://about.digg.com/blog/digg-recommendation-engine-updates
    33.    (Digg Recommendation Engine Updates) P16
    34.   
    35.    http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36955.pdf
    36.    (The Learning Behind Gmail Priority Inbox)p17
    37.   
    38.   http://www.grouplens.org/papers/pdf/mcnee-chi06-acc.pdf
    39.   (Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20
    40.   
    41.   http://www-users.cs.umn.edu/~mcnee/mcnee-cscw2006.pdf
    42.    (Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23
    43.   
    44.   http://www.sigkdd.org/explorations/issues/9-2-2007-12/7-Netflix-2.pdf
    45.    (Major componets of the gravity recommender system) P25
    46.   
    47.   http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext
    48.   (What is a Good Recomendation Algorithm?) P26
    49.   
    50.   http://research.microsoft.com/pubs/115396/evaluationmetrics.tr.pdf
    51.    (Evaluation Recommendation Systems) P27
    52.   
    53.   http://mtg.upf.edu/static/media/PhD_ocelma.pdf
    54.   (Music Recommendation and Discovery in the Long Tail) P29
    55.   
    56.   http://ir.ii.uam.es/divers2011/
    57.   (Internation Workshop on Novelty and Diversity in Recommender Systems) p29
    58.   
    59.   http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_21.pdf
    60.   (Auralist: Introducing Serendipity into Music Recommendation ) P30
    61.   
    62.   http://www.springerlink.com/content/978-3-540-78196-7/#section=239197&page=1&locus=21
    63.   (Metrics for evaluating the serendipity of recommendation lists) P30
    64.   
    65.   http://dare.uva.nl/document/131544
    66.   (The effects of transparency on trust in and acceptance of a content-based art recommender) P31
    67.   
    68.   http://brettb.net/project/papers/2007 Trust-aware recommender systems.pdf
    69.    (Trust-aware recommender systems) P31
    70.   
    71.   http://recsys.acm.org/2011/pdfs/RobustTutorial.pdf
    72.   (Tutorial on robutness of recommender system) P32
    73.   
    74.   http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html
    75.    (Five Stars Dominate Ratings) P37
    76.   
    77.   http://www.informatik.uni-freiburg.de/~cziegler/BX/
    78.   (Book-Crossing Dataset) P38
    79.   
    80.   http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html
    81.   (Lastfm Dataset) P39
    82.   
    83.   http://mmdays.com/2008/11/22/power_law_1/
    84.   (浅谈网络世界的Power Law现象) P39
    85.   
    86.   http://www.grouplens.org/node/73/
    87.   (MovieLens Dataset) P42
    88.   
    89.   http://research.microsoft.com/pubs/69656/tr-98-12.pdf
    90.   (Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49
    91.   
    92.   http://vimeo.com/1242909
    93.   (Digg Vedio) P50
    94.   
    95.   http://glaros.dtc.umn.edu/gkhome/fetch/papers/itemrsCIKM01.pdf
    96.    (Evaluation of Item-Based Top-N Recommendation Algorithms) P58
    97.   
    98.   http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
    99.   (Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59
    100.   
    101.   http://glinden.blogspot.com/2006/03/early-amazon-similarities.html
    102.    (Greg Linden Blog) P63
    103.   
    104.   http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf
    105.   (One-Class Collaborative Filtering) P67
    106.   
    107.   http://en.wikipedia.org/wiki/Stochastic_gradient_descent
    108.   (Stochastic Gradient Descent) P68
    109.   
    110.   http://www.ideal.ece.utexas.edu/seminar/LatentFactorModels.pdf
    111.    (Latent Factor Models for Web Recommender Systems) P70
    112.   
    113.   http://en.wikipedia.org/wiki/Bipartite_graph
    114.   (Bipatite Graph) P73
    115.   
    116.   http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4072747&url=http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4072747
    117.   (Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74
    118.   
    119.   http://www-cs-students.stanford.edu/~taherh/papers/topic-sensitive-pagerank.pdf
    120.   (Topic Sensitive Pagerank) P74
    121.   
    122.   http://www.stanford.edu/dept/ICME/docs/thesis/Li-2009.pdf
    123.   (FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77
    124.   
    125.   https://www.aaai.org/ojs/index.php/aimagazine/article/view/1292
    126.    (LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80
    127.   
    128.   http://research.yahoo.com/files/wsdm266m-golbandi.pdf
    129.   ( adaptive bootstrapping of recommender systems using decision trees) P87
    130.   
    131.   http://en.wikipedia.org/wiki/Vector_space_model
    132.   (Vector Space Model) P90
    133.   
    134.   http://tunedit.org/challenge/VLNetChallenge
    135.   (冷启动问题的比赛) P92
    136.   
    137.   http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf
    138.    (Latent Dirichlet Allocation) P92
    139.   
    140.   http://en.wikipedia.org/wiki/Kullback–Leibler_divergence
    141.    (Kullback–Leibler divergence) P93
    142.   
    143.   http://www.pandora.com/about/mgp
    144.   (About The Music Genome Project) P94
    145.   
    146.   http://en.wikipedia.org/wiki/List_of_Music_Genome_Project_attributes
    147.   (Pandora Music Genome Project Attributes) P94
    148.   
    149.   http://www.jinni.com/movie-genome.html
    150.   (Jinni Movie Genome) P94
    151.   
    152.   http://www.shilad.com/papers/tagsplanations_iui2009.pdf
    153.    (Tagsplanations: Explaining Recommendations Using Tags) P96
    154.   
    155.   http://en.wikipedia.org/wiki/Tag_(metadata)
    156.   (Tag Wikipedia) P96
    157.   
    158.   http://www.shilad.com/shilads_thesis.pdf
    159.   (Nurturing Tagging Communities) P100
    160.   
    161.   http://www.stanford.edu/~morganya/research/chi2007-tagging.pdf
    162.    (Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100
    163.   
    164.   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
    165.   (Delicious Dataset) P101
    166.   
    167.   http://research.microsoft.com/pubs/73692/yihgoca-www06.pdf
    168.    (Finding Advertising Keywords on Web Pages) P118
    169.   
    170.   http://www.kde.cs.uni-kassel.de/ws/rsdc08/
    171.   (基于标签的推荐系统比赛) P119
    172.   
    173.   http://delab.csd.auth.gr/papers/recsys.pdf
    174.   (Tag recommendations based on tensor dimensionality reduction)P119
    175.   
    176.   http://www.l3s.de/web/upload/documents/1/recSys09.pdf
    177.   (latent dirichlet allocation for tag recommendation) P119
    178.   
    179.   http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&rep=rep1&type=pdf
    180.   (Folkrank: A ranking algorithm for folksonomies) P119
    181.   
    182.   http://www.grouplens.org/system/files/tagommenders_numbered.pdf
    183.    (Tagommenders: Connecting Users to Items through Tags) P119
    184.   
    185.   http://www.grouplens.org/system/files/group07-sen.pdf
    186.   (The Quest for Quality Tags) P120
    187.   
    188.   http://2011.camrachallenge.com/
    189.   (Challenge on Context-aware Movie Recommendation) P123
    190.   
    191.   http://bits.blogs.nytimes.com/2011/09/07/the-lifespan-of-a-link/
    192.   (The Lifespan of a link) P125
    193.   
    194.   http://www0.cs.ucl.ac.uk/staff/l.capra/publications/lathia_sigir10.pdf
    195.    (Temporal Diversity in Recommender Systems) P129
    196.   
    197.   http://staff.science.uva.nl/~kamps/ireval/papers/paper_14.pdf
    198.    (Evaluating Collaborative Filtering Over Time) P129
    199.   
    200.   http://www.google.com/places/
    201.   (Hotpot) P139
    202.   
    203.   http://www.readwriteweb.com/archives/google_launches_recommendation_engine_for_places.php
    204.   (Google Launches Hotpot, A Recommendation Engine for Places) P139
    205.   
    206.   http://xavier.amatriain.net/pubs/GeolocatedRecommendations.pdf
    207.    (geolocated recommendations) P140
    208.   
    209.   http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html
    210.   (A Peek Into Netflix Queues) P141
    211.   
    212.   http://www.cs.umd.edu/users/meesh/420/neighbor.pdf
    213.   (Distance Browsing in Spatial Databases1) P142
    214.   
    215.   http://www.eng.auburn.edu/~weishinn/papers/MDM2010.pdf
    216.    (Ef?cient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143
    217.   
    218.   
    219.   http://blog.nielsen.com/nielsenwire/consumer/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most/
    220.   (Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144
    221.   
    222.   http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36371.pdf
    223.   (Suggesting Friends Using the Implicit Social Graph) P145
    224.   
    225.   http://blog.nielsen.com/nielsenwire/online_mobile/friends-frenemies-why-we-add-and-remove-facebook-friends/
    226.   (Friends & Frenemies: Why We Add and Remove Facebook Friends) P147
    227.   
    228.   http://snap.stanford.edu/data/
    229.   (Stanford Large Network Dataset Collection) P149
    230.   
    231.   http://www.dai-labor.de/camra2010/
    232.   (Workshop on Context-awareness in Retrieval and Recommendation) P151
    233.   
    234.   http://www.comp.hkbu.edu.hk/~lichen/download/p245-yuan.pdf
    235.    (Factorization vs. Regularization: Fusing Heterogeneous
    236.   Social Relationships in Top-N Recommendation) P153
    237.   
    238.   http://www.infoq.com/news/2009/06/Twitter-Architecture/
    239.   (Twitter, an Evolving Architecture) P154
    240.   
    241.   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
    242.   (Recommendations in taste related domains) P155
    243.   
    244.   http://www.ercim.eu/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf
    245.   (Comparing Recommendations Made by Online Systems and Friends) P155
    246.   
    247.   http://techcrunch.com/2010/04/22/facebook-edgerank/
    248.   (EdgeRank: The Secret Sauce That Makes Facebook‘s News Feed Tick) P157
    249.   
    250.   http://www.grouplens.org/system/files/p217-chen.pdf
    251.   (Speak Little and Well: Recommending Conversations in Online Social Streams) P158
    252.   
    253.   http://blog.linkedin.com/2008/04/11/learn-more-abou-2/
    254.   (Learn more about “People You May Know”) P160
    255.   
    256.   http://domino.watson.ibm.com/cambridge/research.nsf/58bac2a2a6b05a1285256b30005b3953/8186a48526821924852576b300537839/$FILE/TR 2009.09 Make New Frends.pdf
    257.   (“Make New Friends, but Keep the Old” – Recommending People on Social Networking Sites) P164
    258.   
    259.   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
    260.   (SoRec: Social Recommendation Using Probabilistic Matrix) P165
    261.   
    262.   http://olivier.chapelle.cc/pub/DBN_www2009.pdf
    263.   (A Dynamic Bayesian Network Click Model for Web Search Ranking) P177
    264.   
    265.   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
    266.   (Online Learning from Click Data for Sponsored Search) P177
    267.   
    268.   http://www.cs.cmu.edu/~deepay/mywww/papers/www08-interaction.pdf
    269.   (Contextual Advertising by Combining Relevance with Click Feedback) P177
    270.   http://tech.hulu.com/blog/2011/09/19/recommendation-system/
    271.   (Hulu 推荐系统架构) P178
    272.   
    273.   http://mymediaproject.codeplex.com/
    274.   (MyMedia Project) P178
    275.   
    276.   http://www.grouplens.org/papers/pdf/www10_sarwar.pdf
    277.   (item-based collaborative filtering recommendation algorithms) P185
    278.   
    279.   http://www.stanford.edu/~koutrika/Readings/res/Default/billsus98learning.pdf
    280.   (Learning Collaborative Information Filters) P186
    281.   
    282.   http://sifter.org/~simon/journal/20061211.html
    283.   (Simon Funk Blog:Funk SVD) P187
    284.   
    285.   http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf
    286.   (Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190
    287.   
    288.   http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh26.pdf
    289.   (Time-dependent Models in Collaborative Filtering based Recommender System) P193
    290.   
    291.   http://sydney.edu.au/engineering/it/~josiah/lemma/kdd-fp074-koren.pdf
    292.   (Collaborative filtering with temporal dynamics) P193
    293.   
    294.   http://en.wikipedia.org/wiki/Least_squares
    295.   (Least Squares Wikipedia) P195
    296.   
    297.   http://www.mimuw.edu.pl/~paterek/ap_kdd.pdf
    298.   (Improving regularized singular value decomposition for collaborative filtering) P195
    299.   
    300.   http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf
    301.    (Factorization Meets the Neighborhood: a Multifaceted
    302.   Collaborative Filtering Model) P195

    复制代码

   

沙发

 发表于 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
各个领域的推荐系统

图书

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新闻

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时间: 2024-10-11 01:26:48

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