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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. 　　 
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    278. 　　 
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    280. 　　(Learning Collaborative Information Filters) P186 
    281. 　　 
    282. 　　http://sifter.org/~simon/journal/20061211.html 
    283. 　　(Simon Funk Blog:Funk SVD) P187 
    284. 　　 
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    286. 　　(Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190 
    287. 　　 
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    290. 　　 
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    293. 　　 
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    296. 　　 
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    298. 　　(Improving regularized singular value decomposition for collaborative filtering) P195 
    299. 　　 
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    301. 　　 (Factorization Meets the Neighborhood: a Multifaceted 
    302. 　　Collaborative Filtering Model) P195
    复制代码
 
 
   
沙发
 
技术分享 发表于 2014-3-19 11:59:18

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