J.Boyan D.Freitag T.Joachims A Machine Learning Architecture for Optimizing Web Search Engines 1996 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... ThomasFinley ThorstenJoachims Training structural SVMs when exact inference is intractable. ACM 307 304-311 2008 T.Joachims Evaluating Retrieval Performance Using Clickthrough Data 2002 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... ThorstenJoachims Text categorization with support vector machines: learning with many relevant features Springer 137–142 1998 ThorstenJoachims Making large-scale support vector machine learning practical MIT Press 169–184 1998 ThorstenJoachims A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization Morgan Kaufmann Publishers, San Francisco, US 143–151 1997 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... ThorstenJoachims LauraGranka BingPan HeleneHembrooke GeriGay Accurately interpreting clickthrough data as implicit feedback ACM Press 154–161 2005 FilipRadlinski ThorstenJoachims Active exploration for learning rankings from clickthrough data ACM Press 570–579 2007 FilipRadlinski RobertKleinberg ThorstenJoachims Learning diverse rankings with multi-armed bandits. ACM 307 784-791 2008 YisongYue ThorstenJoachims Predicting diverse subsets using structural SVMs. ACM 307 1224-1231 2008