Big-Data-Resources 墨蓝 2022-07-12 05:39 206阅读 0赞 \#大数据/数据挖掘/推荐系统/机器学习相关资源 Share my personal resources \#\#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> \* 机器学习&深度学习经典资料汇总 <http://www.thebigdata.cn/JiShuBoKe/13299.html> \#视频 \* 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> \#\#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 \#\#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> \#\#\#库 \* NLTK <https://github.com/nltk/nltk> \* Pattern <https://github.com/clips/pattern> \* Pyrallel <https://github.com/pydata/pyrallel> \* Theano <https://github.com/Theano/Theano> \* Pylearn2 <https://github.com/lisa-lab/pylearn2> \* TextBlob <https://github.com/sloria/TextBlob> \* MBSP <https://github.com/clips/MBSP> \* Gensim <https://github.com/piskvorky/gensim> \* Langid.py <https://github.com/saffsd/langid.py> \* Jieba <https://github.com/fxsjy/jieba> \* xTAS <https://github.com/NLeSC/xtas> \* NumPy <https://github.com/numpy/numpy> \* SciPy <https://github.com/scipy/scipy> \* Matplotlib <https://github.com/matplotlib/matplotlib> \* scikit-learn <https://github.com/scikit-learn/scikit-learn> \* Pandas <https://github.com/pydata/pandas> \* MDP <http://mdp-toolkit.sourceforge.net/> \* PyBrain <https://github.com/pybrain/pybrain> \* PyML <http://pyml.sourceforge.net/> \* Milk <https://github.com/luispedro/milk> \* PyMVPA <https://github.com/PyMVPA/PyMVPA> \#\# 博客 \* 周涛 <http://blog.sciencenet.cn/home.php?mod=space&uid=3075> \* Greg Linden <http://glinden.blogspot.com/> \* Marcel Caraciolo <http://aimotion.blogspot.com/> \* RsysChina <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> \* Journal of Machine Learning Research <http://jmlr.org/> \* 在线的机器学习社区 <http://www.52ml.net/16336.html> \* 清华大学信息检索组 <http://www.thuir.cn> \* 我爱自然语言处理 <http://www.52nlp.cn/> \#\#文章 \* 心中永远的正能量 <http://blog.csdn.net/yunlong34574> \* 机器学习最佳入门学习资料汇总 <http://article.yeeyan.org/view/22139/410514> \* Books for Machine Learning with R <http://www.52ml.net/16312.html> \* 是什么阻碍了你的机器学习目标？ <http://www.52ml.net/16436.htm> \* 推荐系统初探 <http://yongfeng.me/attach/rs-survey-zhang-slices.pdf> \* 推荐系统中协同过滤算法若干问题的研究 <http://pan.baidu.com/s/1bnjDBYZ> \* 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., 2003 <http://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> \#\# 论文 《推荐系统实战》引用 \* \[P1\](http://en.wikipedia.org/wiki/Information\_overload) \* \[A Guide to Recommender System P4\](http://www.readwriteweb.com/archives/recommender\_systems.php) \* \[Cross Selling P6\](http://en.wikipedia.org/wiki/Cross-selling) \* \[课程：Data Mining and E-Business: The Social Data Revolution P7)\](http://stanford2009.wikispaces.com/ ) \* \[An Introduction to Search Engines and Web Navigation p7\](http://thesearchstrategy.com/ebooks/an%20introduction%20to%20search%20engines%20and%20web%20navigation.pdf) \* \[p8\](http://www.netflixprize.com/) \* \[p9\](http://cdn-0.nflximg.com/us/pdf/Consumer\_Press\_Kit.pdf) \* \[(The Youtube video recommendation system) p9\](http://stuyresearch.googlecode.com/hg-history/c5aa9d65d48c787fd72dcd0ba3016938312102bd/blake/resources/p293-davidson.pdf) \* \[(PPT: Music Recommendation and Discovery) p12\](http://www.slideshare.net/plamere/music-recommendation-and-discovery) \* \[P13\](http://www.facebook.com/instantpersonalization/) \* \[(Digg Recommendation Engine Updates) P16\](http://about.digg.com/blog/digg-recommendation-engine-updates) \* \[(The Learning Behind Gmail Priority Inbox)p17\](http://static.googleusercontent.com/external\_content/untrusted\_dlcp/research.google.com/en//pubs/archive/36955.pdf) \* \[(Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20\](http://www.grouplens.org/papers/pdf/mcnee-chi06-acc.pdf) \* \[(Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23\](http://www-users.cs.umn.edu/~mcnee/mcnee-cscw2006.pdf) \* \[(Major componets of the gravity recommender system) P25\](http://www.sigkdd.org/explorations/issues/9-2-2007-12/7-Netflix-2.pdf) \* \[(What is a Good Recomendation Algorithm?) P26\](http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext) \* \[(Evaluation Recommendation Systems) P27\](http://research.microsoft.com/pubs/115396/evaluationmetrics.tr.pdf ) \* \[(Music Recommendation and Discovery in the Long Tail) P29\](http://mtg.upf.edu/static/media/PhD\_ocelma.pdf) \* \[(Internation Workshop on Novelty and Diversity in Recommender Systems) p29\](http://ir.ii.uam.es/divers2011/) \* \[(Auralist: Introducing Serendipity into Music Recommendation ) P30\](http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research\_Notes/RN\_11\_21.pdf) \* \[(Metrics for evaluating the serendipity of recommendation lists) P30\](http://www.springerlink.com/content/978-3-540-78196-7/\#section=239197&page=1&locus=21) \* \[(The effects of transparency on trust in and acceptance of a content-based art recommender) P31\](http://dare.uva.nl/document/131544) \* \[(Trust-aware recommender systems) P31\](http://brettb.net/project/papers/2007%20Trust-aware%20recommender%20systems.pdf) \* \[(Tutorial on robutness of recommender system) P32\](http://recsys.acm.org/2011/pdfs/RobustTutorial.pdf) \* \[(Five Stars Dominate Ratings) P37 \](http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html) \* \[(Book-Crossing Dataset) P38 \](http://www.informatik.uni-freiburg.de/~cziegler/BX/) \* \[(Lastfm Dataset) P39\](http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html) \* \[浅谈网络世界的Power Law现象 P39\](http://mmdays.com/2008/11/22/power\_law\_1/) \* \[(MovieLens Dataset) P42\](http://www.grouplens.org/node/73/) \* \[(Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49\](http://research.microsoft.com/pubs/69656/tr-98-12.pdf) \* \[(Digg Vedio) P50\](http://vimeo.com/1242909) \* \[(Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59\](http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf) \* \[(Greg Linden Blog) P63\](http://glinden.blogspot.com/2006/03/early-amazon-similarities.html) \* \[(One-Class Collaborative Filtering) P67\](http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf) \* \[(Stochastic Gradient Descent) P68 \](http://en.wikipedia.org/wiki/Stochastic\_gradient\_descent) \* \[(Latent Factor Models for Web Recommender Systems) P70 \](http://www.ideal.ece.utexas.edu/seminar/LatentFactorModels.pdf) \* \[(Bipatite Graph) P73\](http://en.wikipedia.org/wiki/Bipartite\_graph) \* \[(Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74\](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4072747&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs\_all.jsp%3Farnumber%3D4072747) \* \[(Topic Sensitive Pagerank) P74\](http://www-cs-students.stanford.edu/~taherh/papers/topic-sensitive-pagerank.pdf) \* \[(FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77\](http://www.stanford.edu/dept/ICME/docs/thesis/Li-2009.pdf) \* \[(LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80\](https://www.aaai.org/ojs/index.php/aimagazine/article/view/1292) \* \[( adaptive bootstrapping of recommender systems using decision trees) P87\](http://research.yahoo.com/files/wsdm266m-golbandi.pdf) \* \[(Vector Space Model) P90\](http://en.wikipedia.org/wiki/Vector\_space\_model) \* \[(冷启动问题的比赛) P92\](http://tunedit.org/challenge/VLNetChallenge ) \* \[(Latent Dirichlet Allocation) P92\](http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) \* \[(Kullback–Leibler divergence) P93\](http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler\_divergence) \* \[(About The Music Genome Project) P94\](http://www.pandora.com/about/mgp) \* \[(Pandora Music Genome Project Attributes) P94\](http://en.wikipedia.org/wiki/List\_of\_Music\_Genome\_Project\_attributes ) \* \[(Jinni Movie Genome) P94\](http://www.jinni.com/movie-genome.html) \* \[(Tagsplanations: Explaining Recommendations Using Tags) P96\](http://www.shilad.com/papers/tagsplanations\_iui2009.pdf) \* \[(Tag Wikipedia) P96\](http://en.wikipedia.org/wiki/Tag\_(metadata)) \* \[(Nurturing Tagging Communities) P100\](http://www.shilad.com/shilads\_thesis.pdf ) \* \[(Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100\](http://www.stanford.edu/~morganya/research/chi2007-tagging.pdf ) \* \[(Delicious Dataset) P101\](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) \* \[(Finding Advertising Keywords on Web Pages) P118\](http://research.microsoft.com/pubs/73692/yihgoca-www06.pdf ) \* \[(基于标签的推荐系统比赛) P119\](http://www.kde.cs.uni-kassel.de/ws/rsdc08/ ) \* \[(Tag recommendations based on tensor dimensionality reduction）P119\](http://delab.csd.auth.gr/papers/recsys.pdf) \* \[(latent dirichlet allocation for tag recommendation) P119\](http://www.l3s.de/web/upload/documents/1/recSys09.pdf) \* \[(Folkrank: A ranking algorithm for folksonomies) P119\](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&rep=rep1&type=pdf) \* \[(Tagommenders: Connecting Users to Items through Tags) P119\](http://www.grouplens.org/system/files/tagommenders\_numbered.pdf) \* \[(The Quest for Quality Tags) P120\](http://www.grouplens.org/system/files/group07-sen.pdf) \* \[(Challenge on Context-aware Movie Recommendation) P123\](http://2011.camrachallenge.com/) \* \[(The Lifespan of a link) P125\](http://bits.blogs.nytimes.com/2011/09/07/the-lifespan-of-a-link/ ) \* \[(Temporal Diversity in Recommender Systems) P129\](http://www0.cs.ucl.ac.uk/staff/l.capra/publications/lathia\_sigir10.pdf ) \* \[(Evaluating Collaborative Filtering Over Time) P129\](http://staff.science.uva.nl/~kamps/ireval/papers/paper\_14.pdf ) \* \[(Hotpot) P139 \](http://www.google.com/places/ ) \* \[(Google Launches Hotpot, A Recommendation Engine for Places) P139\](http://www.readwriteweb.com/archives/google\_launches\_recommendation\_engine\_for\_places.php) \* \[(geolocated recommendations) P140\](http://xavier.amatriain.net/pubs/GeolocatedRecommendations.pdf ) \* \[(A Peek Into Netflix Queues) P141\](http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html ) \* \[(Distance Browsing in Spatial Databases1) P142\](http://www.cs.umd.edu/users/meesh/420/neighbor.pdf ) \* \[(Efficient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143\](http://www.eng.auburn.edu/~weishinn/papers/MDM2010.pdf ) \* \[(Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144 \](http://blog.nielsen.com/nielsenwire/consumer/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most/ ) \* \[(Suggesting Friends Using the Implicit Social Graph) P145\](http://static.googleusercontent.com/external\_content/untrusted\_dlcp/research.google.com/en//pubs/archive/36371.pdf ) \* \[(Friends & Frenemies: Why We Add and Remove Facebook Friends) P147\](http://blog.nielsen.com/nielsenwire/online\_mobile/friends-frenemies-why-we-add-and-remove-facebook-friends/ ) \* \[(Stanford Large Network Dataset Collection) P149 \](http://snap.stanford.edu/data/ ) \* \[(Workshop on Context-awareness in Retrieval and Recommendation) P151\](http://www.dai-labor.de/camra2010/ ) \* \[(Factorization vs. Regularization: Fusing Heterogeneous Social Relationships in Top-N Recommendation) P153 \](http://www.comp.hkbu.edu.hk/~lichen/download/p245-yuan.pdf ) \* \[(Twitter, an Evolving Architecture) P154\](http://www.infoq.com/news/2009/06/Twitter-Architecture/ ) \* \[(Recommendations in taste related domains) P155\](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 ) \* \[(Comparing Recommendations Made by Online Systems and Friends) P155\](http://www.ercim.eu/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf) \* \[(EdgeRank: The Secret Sauce That Makes Facebook's News Feed Tick) P157\](http://techcrunch.com/2010/04/22/facebook-edgerank/ ) \* \[(Speak Little and Well: Recommending Conversations in Online Social Streams) P158\](http://www.grouplens.org/system/files/p217-chen.pdf ) \* \[(Learn more about “People You May Know”) P160\](http://blog.linkedin.com/2008/04/11/learn-more-abou-2/ ) \* \[("Make New Friends, but Keep the Old" – Recommending People on Social Networking Sites) P164 \](http://domino.watson.ibm.com/cambridge/research.nsf/58bac2a2a6b05a1285256b30005b3953/8186a48526821924852576b300537839/$FILE/TR%202009.09%20Make%20New%20Frends.pdf) \* \[(SoRec: Social Recommendation Using Probabilistic Matrix) P165 \](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) \* \[(A Dynamic Bayesian Network Click Model for Web Search Ranking) P177\](http://olivier.chapelle.cc/pub/DBN\_www2009.pdf ) \* \[(Online Learning from Click Data for Sponsored Search) P177\](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) \* \[(Contextual Advertising by Combining Relevance with Click Feedback) P177 \](http://www.cs.cmu.edu/~deepay/mywww/papers/www08-interaction.pdf) \* \[(Hulu 推荐系统架构) P178\](http://tech.hulu.com/blog/2011/09/19/recommendation-system/ ) \* \[(MyMedia Project) P178\](http://mymediaproject.codeplex.com/) \* \[(item-based collaborative filtering recommendation algorithms) P185\](http://www.grouplens.org/papers/pdf/www10\_sarwar.pdf ) \* \[(Learning Collaborative Information Filters) P186 \](http://www.stanford.edu/~koutrika/Readings/res/Default/billsus98learning.pdf ) \* \[(Simon Funk Blog:Funk SVD) P187 \](http://sifter.org/~simon/journal/20061211.html ) \* \[(Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190 \](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf ) \* \[(Time-dependent Models in Collaborative Filtering based Recommender System) P193 \](http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh26.pdf ) \* \[(Collaborative filtering with temporal dynamics) P193\](http://sydney.edu.au/engineering/it/~josiah/lemma/kdd-fp074-koren.pdf ) \* \[(Least Squares Wikipedia) P195\](http://en.wikipedia.org/wiki/Least\_squares ) \* \[(Improving regularized singular value decomposition for collaborative filtering) P195\](http://www.mimuw.edu.pl/~paterek/ap\_kdd.pdf ) \* \[(Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model) P195\](http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf ) <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90ACM%20RecSys%202009%20Workshop%E3%80%91Improving%20recommendation%20accuracy%20by%20clustering%20so.pdf&id=37991" target="\_blank"> </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20Best%20Stu%20Paper%E3%80%91Incorporating%20Occupancy%20into%20Frequent%20Pattern%20Mini.pdf&id=37992" target="\_blank"> 【CIKM 2012 Best Stu Paper】Incorporating Occupancy into Frequent Pattern Mini.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91A%20Latent%20Pairwise%20Preference%20Learning%20Approach%20for%20Recomme.pdf&id=37993" target="\_blank"> 【CIKM 2012 poster】A Latent Pairwise Preference Learning Approach for Recomme.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91An%20Effective%20Category%20Classification%20Method%20Based%20on%20a%20Lan.pdf&id=37994" target="\_blank"> 【CIKM 2012 poster】An Effective Category Classification Method Based on a Lan.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91Learning%20to%20Rank%20for%20Hybrid%20Recommendation.pdf&id=37995" target="\_blank"> 【CIKM 2012 poster】Learning to Rank for Hybrid Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91Learning%20to%20Recommend%20with%20Social%20Relation%20Ensemble.pdf&id=37996" target="\_blank"> 【CIKM 2012 poster】Learning to Recommend with Social Relation Ensemble.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91Maximizing%20Revenue%20from%20Strategic%20Recommendations%20under%20De.pdf&id=37997" target="\_blank"> 【CIKM 2012 poster】Maximizing Revenue from Strategic Recommendations under De.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91On%20Using%20Catexperts%20for%20Improving%20the%20Performance%20an.pdf&id=37998" target="\_blank"> 【CIKM 2012 poster】On Using Category Experts for Improving the Performance an.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91Relation%20Regularized%20Subspace%20Recommending%20for%20Related%20Sci.pdf&id=37999" target="\_blank"> 【CIKM 2012 poster】Relation Regularized Subspace Recommending for Related Sci.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91Top-N%20Recommendation%20through%20Belief%20Propagation.pdf&id=38000" target="\_blank"> 【CIKM 2012 poster】Top-N Recommendation through Belief Propagation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20poster%E3%80%91Twitter%20Hyperlink%20Recommendation%20with%20User-Tweet-Hyperlink.pdf&id=38001" target="\_blank"> 【CIKM 2012 poster】Twitter Hyperlink Recommendation with User-Tweet-Hyperlink.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91Automatic%20Query%20Expansion%20Based%20on%20Tag%20Recommendation.pdf&id=38002" target="\_blank"> 【CIKM 2012 short】Automatic Query Expansion Based on Tag Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91Graph-Based%20Workflow%20Recommendation-%20On%20Improving%20Business%20.pdf&id=38003" target="\_blank"> 【CIKM 2012 short】Graph-Based Workflow Recommendation- On Improving Business .pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91Location-Sensitive%20Resources%20Recommendation%20in%20Social%20Taggi.pdf&id=38004" target="\_blank"> 【CIKM 2012 short】Location-Sensitive Resources Recommendation in Social Taggi.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91More%20Than%20Relevance-%20High%20Utility%20Query%20Recommendation%20By%20M.pdf&id=38005" target="\_blank"> 【CIKM 2012 short】More Than Relevance- High Utility Query Recommendation By M.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91PathRank-%20A%20Novel%20Node%20Ranking%20Measure%20on%20a%20Heterogeneous%20G.pdf&id=38006" target="\_blank"> 【CIKM 2012 short】PathRank- A Novel Node Ranking Measure on a Heterogeneous G.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91PRemiSE-%20Personalized%20News%20Recommendation%20via%20Implicit%20Soci.pdf&id=38007" target="\_blank"> 【CIKM 2012 short】PRemiSE- Personalized News Recommendation via Implicit Soci.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91Query%20Recommendation%20for%20Children.pdf&id=38008" target="\_blank"> 【CIKM 2012 short】Query Recommendation for Children.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91The%20Early-Adopter%20Graph%20and%20its%20Application%20to%20Web-Page%20Rec.pdf&id=38009" target="\_blank"> 【CIKM 2012 short】The Early-Adopter Graph and its Application to Web-Page Rec.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91Time-aware%20Topic%20Recommendation%20Based%20on%20Micro-blogs.pdf&id=38010" target="\_blank"> 【CIKM 2012 short】Time-aware Topic Recommendation Based on Micro-blogs.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%20short%E3%80%91Using%20Program%20Synthesis%20for%20Social%20Recommendations.pdf&id=38011" target="\_blank"> 【CIKM 2012 short】Using Program Synthesis for Social Recommendations.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91A%20Decentralized%20Recommender%20System%20for%20Effective%20Web%20Credibility%20.pdf&id=38012" target="\_blank"> 【CIKM 2012】A Decentralized Recommender System for Effective Web Credibility .pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91A%20Generalized%20Framework%20for%20Reciprocal%20Recommender%20Systems.pdf&id=38013" target="\_blank"> 【CIKM 2012】A Generalized Framework for Reciprocal Recommender Systems.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91Dynamic%20Covering%20for%20Recommendation%20Systems.pdf&id=38014" target="\_blank"> 【CIKM 2012】Dynamic Covering for Recommendation Systems.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91Efficient%20Retri.%20of%20Recommendations%20in%20a%20Matrix%20Factorization%20.pdf&id=38015" target="\_blank"> 【CIKM 2012】Efficient Retrieval of Recommendations in a Matrix Factorization .pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91Exploring%20Personal%20Impact%20for%20Group%20Recommendation.pdf&id=38016" target="\_blank"> 【CIKM 2012】Exploring Personal Impact for Group Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91LogUCB-%20An%20Explore-Exploit%20Algorithm%20For%20Comments%20Recommendation.pdf&id=38017" target="\_blank"> 【CIKM 2012】LogUCB- An Explore-Exploit Algorithm For Comments Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91Metaphor-%20A%20System%20for%20Related%20Search%20Recommendations.pdf&id=38018" target="\_blank"> 【CIKM 2012】Metaphor- A System for Related Search Recommendations.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91Social%20Contextual%20Recommendation.pdf&id=38019" target="\_blank"> 【CIKM 2012】Social Contextual Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90CIKM%202012%E3%80%91Social%20Recommendation%20Across%20Multiple%20Relational%20Domains.pdf&id=38020" target="\_blank"> 【CIKM 2012】Social Recommendation Across Multiple Relational Domains.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90COMMUNICATIONS%20OF%20THE%20ACM%E3%80%91Recommender%20Systems.pdf&id=38021" target="\_blank"> 【COMMUNICATIONS OF THE ACM】Recommender Systems.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90ICDM%202012%20short\_\_\_%E3%80%91Multiplicative%20Algorithms%20for%20Constrained%20Non-negative%20M.pdf&id=38022" target="\_blank"> 【ICDM 2012 short\_\_\_】Multiplicative Algorithms for Constrained Non-negative M.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90ICDM%202012%20short%E3%80%91Collaborative%20Filtering%20with%20Aspect-based%20Opinion%20Mining-%20A.pdf&id=38023" target="\_blank"> 【ICDM 2012 short】Collaborative Filtering with Aspect-based Opinion Mining- A.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90ICDM%202012%20short%E3%80%91Learning%20Heterogeneous%20Similarity%20Measures%20for%20Hybrid-Recom.pdf&id=38024" target="\_blank"> 【ICDM 2012 short】Learning Heterogeneous Similarity Measures for Hybrid-Recom.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90ICDM%202012%20short%E3%80%91Mining%20Personal%20Context-Aware%20Preferences%20for%20Mobile%20Users.pdf&id=38025" target="\_blank"> 【ICDM 2012 short】Mining Personal Context-Aware Preferences for Mobile Users.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90ICDM%202012%E3%80%91Link%20Prediction%20and%20Recommendation%20across%20Heterogenous%20Social%20Networks.pdf&id=38026" target="\_blank"> 【ICDM 2012】Link Prediction and Recommendation across Heterogenous Social Networks.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90IEEE%20Computer%20Society%202009%E3%80%91Matrix%20factorization%20techniques%20for%20recommender%20.pdf&id=38027" target="\_blank"> 【IEEE Computer Society 2009】Matrix factorization techniques for recommender .pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90IEEE%20Consumer%20Communications%20and%20Networking%20Conference%202006%E3%80%91FilmTrust%20movie.pdf&id=38028" target="\_blank"> 【IEEE Consumer Communications and Networking Conference 2006】FilmTrust movie.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90IEEE%20Trans%20on%20Audio%2C%20Speech%20and%20Laguage%20Processing%202010%E3%80%91Personalized%20music%20.pdf&id=38029" target="\_blank"> 【IEEE Trans on Audio, Speech and Laguage Processing 2010】Personalized music .pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90IEEE%20Transactions%20on%20Knowledge%20and%20Data%20Engineering%202005%E3%80%91Toward%20the%20next%20ge.pdf&id=38030" target="\_blank"> 【IEEE Transactions on Knowledge and Data Engineering 2005】Toward the next ge.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90INFOCOM%202011%E3%80%91Bayesian-inference%20Based%20Recommendation%20in%20Online%20Social%20Network.pdf&id=38031" target="\_blank"> 【INFOCOM 2011】Bayesian-inference Based Recommendation in Online Social Network.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90KDD%202009%E3%80%91Learning%20optimal%20ranking%20with%20tensor%20factorization%20for%20tag%20recomme.pdf&id=38032" target="\_blank"> 【KDD 2009】Learning optimal ranking with tensor factorization for tag recomme.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202009%E3%80%91Learning%20to%20Recommend%20with%20Social%20Trust%20Ensemble.pdf&id=38033" target="\_blank"> 【SIGIR 2009】Learning to Recommend with Social Trust Ensemble.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Adaptive%20Diversification%20of%20Recommendation%20Results%20via%20Latent%20Fa.pdf&id=38034" target="\_blank"> 【SIGIR 2012】Adaptive Diversification of Recommendation Results via Latent Fa.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Collaborative%20Personalized%20Tweet%20Recommendation.pdf&id=38035" target="\_blank"> 【SIGIR 2012】Collaborative Personalized Tweet Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Dual%20Role%20Model%20for%20Question%20Recommendation%20in%20Community%20Questio.pdf&id=38036" target="\_blank"> 【SIGIR 2012】Dual Role Model for Question Recommendation in Community Questio.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Exploring%20Social%20Influence%20for%20Recommendation%20-%20A%20Generative%20Mod.pdf&id=38037" target="\_blank"> 【SIGIR 2012】Exploring Social Influence for Recommendation - A Generative Mod.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Increasing%20Temporal%20Diversity%20with%20Purchase%20Intervals.pdf&id=38038" target="\_blank"> 【SIGIR 2012】Increasing Temporal Diversity with Purchase Intervals.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Learning%20to%20Rank%20Social%20Update%20Streams.pdf&id=38039" target="\_blank"> 【SIGIR 2012】Learning to Rank Social Update Streams.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Personalized%20Click%20Shaping%20through%20Lagrangian%20Duality%20for%20Online.pdf&id=38040" target="\_blank"> 【SIGIR 2012】Personalized Click Shaping through Lagrangian Duality for Online.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91Predicting%20the%20Ratings%20of%20Multimedia%20Items%20for%20Making%20Personaliz.pdf&id=38041" target="\_blank"> 【SIGIR 2012】Predicting the Ratings of Multimedia Items for Making Personaliz.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91TFMAP-Optimizing%20MAP%20for%20Top-N%20Context-aware%20Recommendation.pdf&id=38042" target="\_blank"> 【SIGIR 2012】TFMAP-Optimizing MAP for Top-N Context-aware Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGIR%202012%E3%80%91What%20Reviews%20are%20Satisfactory-%20Novel%20Features%20for%20Automatic%20Help.pdf&id=38043" target="\_blank"> 【SIGIR 2012】What Reviews are Satisfactory- Novel Features for Automatic Help.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGKDD%202012%E3%80%91%20A%20Semi-Supervised%20Hybrid%20Shilling%20Attack%20Detector%20for%20Trustwor.pdf&id=38044" target="\_blank"> 【SIGKDD 2012】 A Semi-Supervised Hybrid Shilling Attack Detector for Trustwor.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGKDD%202012%E3%80%91%20RecMax-%20Exploiting%20Recommender%20Systems%20for%20Fun%20and%20Profit.pdf&id=38045" target="\_blank"> 【SIGKDD 2012】 RecMax- Exploiting Recommender Systems for Fun and Profit.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGKDD%202012%E3%80%91Circle-based%20Recommendation%20in%20Online%20Social%20Networks.pdf&id=38046" target="\_blank"> 【SIGKDD 2012】Circle-based Recommendation in Online Social Networks.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGKDD%202012%E3%80%91Cross-domain%20Collaboration%20Recommendation.pdf&id=38047" target="\_blank"> 【SIGKDD 2012】Cross-domain Collaboration Recommendation.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGKDD%202012%E3%80%91Finding%20Trending%20Local%20Topics%20in%20Search%20Queries%20for%20Personaliza.pdf&id=38048" target="\_blank"> 【SIGKDD 2012】Finding Trending Local Topics in Search Queries for Personaliza.pdf </a> </p> <p style="line-height:16px;"> <a href="http://blog.sciencenet.cn/home.php?mod=attachment&filename=%E3%80%90SIGKDD%202012%E3%80%91GetJar%20Mobile%20Application%20Recommendations%20with%20Very%20Sparse%20Datasets.pdf&id=38049" target="\_blank"> 【SIGKDD 2012】GetJar Mobile Application Recommendations with Very Sparse Datasets.pdf </a> </p> <p style="line-height:16px;"> <a 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