BibliographyType,ISBN,Identifier,Author,Title,Journal,Volume,Number,Month,Pages,Year,Address,Note,URL,Booktitle,Chapter,Edition,Series,Editor,Publisher,ReportType,Howpublished,Institution,Organizations,School,Annote,Custom1,Custom2,Custom3,Custom4,Custom5
6,"","citeulike:1711976","Boyan, J.; Freitag, D. & Joachims, T.","A Machine Learning Architecture for Optimizing Web Search Engines","",,,"","",1996,"","","http://citeseer.ist.psu.edu/boyan96machine.html","Proceedings of the AAAI Workshop on Internet-Based Information Systems","","","","","","","","","","","","Indexing systems for the World Wide Web, such as Lycos and Alta Vista, play an essential role in making the Web useful and usable. These systems are based on Information Retrieval methods for indexing plain text documents, but also include heuristics for adjusting their document rankings based on the special HTML structure of Web documents. In this paper, we describe a wide range of such heuristics---including a novel one inspired by reinforcement learning techniques for propagating rewards...","","feedback, learning, machine, reinforcement, relevance, search, web ","",""
6,"978-1-60558-205-4","conf/icml/FinleyJ08","Finley, Thomas & Joachims, Thorsten","Training structural SVMs when exact inference is intractable.","",307,,"","304-311",2008,"","","http://dblp.uni-trier.de/db/conf/icml/icml2008.html#FinleyJ08","ICML","","","ACM International Conference Proceeding Series","Cohen, William W.; McCallum, Andrew & Roweis, Sam T.","ACM","","","","","","","","","dblp ","",""
10,"","citeulike:460217","Joachims, T.","Evaluating Retrieval Performance Using Clickthrough Data","",,,"","",2002,"","","http://citeseer.ist.psu.edu/667182.html","","","","","","","","","","","","","This paper proposes a new method for evaluating the quality of retrieval
functions. Unlike traditional methods that require relevance judgments
by experts or explicit user feedback, it is based entirely on clickthrough
data. This is a key advantage, since clickthrough data can be
collected at very low cost and without overhead for the user. Taking
an approach from experiment design, the paper proposes an experiment
setup that generates unbiased feedback about the relative quality of two...","","clickthrough, evaluation, feedback, implicit, information, performance, relevance, retrieval ","",""
6,"","joachims98a","Joachims, Thorsten","Text categorization with support vector machines: learning with many relevant features","",,,"","137--142",1998,"Heidelberg et al.","","http://citeseer.ist.psu.edu/joachims97text.html","Proceedings of {ECML}-98, 10th European Conference on Machine Learning","","","","Nedellec, Claire & Rouveirol, Céline","Springer","","","","","","","","","machine_learning svm text_mining ","",""
7,"0-262-19416-3","joachims98b","Joachims, Thorsten","Making large-scale support vector machine learning practical","",,,"","169--184",1998,"Cambridge, MA, USA","","http://portal.acm.org/citation.cfm?id=299104#","","","","","","MIT Press","","","","","","","","","machine_learning programming tools ","",""
6,"","citeulike:1711972","Joachims, Thorsten","A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization","",,,"","143--151",1997,"Nashville, US","","http://citeseer.ist.psu.edu/54920.html","Proceedings of ICML-97, 14th International Conference on Machine Learning","","","","Fisher, Douglas H.","Morgan Kaufmann Publishers, San Francisco, US","","","","","","","The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classifier. The Rocchio...","","categorization, feedback, learning, machine, relevance, rocchio, text ","",""
6,"1595930345","citeulike:379395","Joachims, Thorsten; Granka, Laura; Pan, Bing; Hembrooke, Helene & Gay, Geri","Accurately interpreting clickthrough data as implicit feedback","",,,"","154--161",2005,"New York, NY, USA","","http://portal.acm.org/citation.cfm?id=1076063","SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval","","","","","ACM Press","","","","","","","","","clickthrough, feedback, implicit, information, relevance, retrieval, search, web ","",""
6,"9781595936097","citeulike:1659403","Radlinski, Filip & Joachims, Thorsten","Active exploration for learning rankings from clickthrough data","",,,"","570--579",2007,"New York, NY, USA","","http://portal.acm.org/citation.cfm?id=1281254","KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining","","","","","ACM Press","","","","","","","","review on geeking with greg: http://glinden.blogspot.com/2007/09/actively-learning-to-rank.html","clickthrough, feedback, learning, machine, relevance ","",""
6,"978-1-60558-205-4","conf/icml/RadlinskiKJ08","Radlinski, Filip; Kleinberg, Robert & Joachims, Thorsten","Learning diverse rankings with multi-armed bandits.","",307,,"","784-791",2008,"","","http://dblp.uni-trier.de/db/conf/icml/icml2008.html#RadlinskiKJ08","ICML","","","ACM International Conference Proceeding Series","Cohen, William W.; McCallum, Andrew & Roweis, Sam T.","ACM","","","","","","","","","dblp ","",""
6,"978-1-60558-205-4","conf/icml/YueJ08","Yue, Yisong & Joachims, Thorsten","Predicting diverse subsets using structural SVMs.","",307,,"","1224-1231",2008,"","","http://dblp.uni-trier.de/db/conf/icml/icml2008.html#YueJ08","ICML","","","ACM International Conference Proceeding Series","Cohen, William W.; McCallum, Andrew & Roweis, Sam T.","ACM","","","","","","","","","dblp ","",""