Titlebook: Learning to Rank for Information Retrieval; Tie-Yan Liu Book 20111st edition Springer-Verlag Berlin Heidelberg 2011 Information Retrieval.

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Relational Rankingg process, but also considers the inter-relationship between documents. According to different relationships (e.g., similarity, preference, and dissimilarity), the task may correspond to different real applications (e.g., pseudo relevance feedback, topic distillation, and search result diversificati

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Query-Dependent Ranking use a single ranking function to deal with all kinds of queries. Instead, one may achieve performance gain by leveraging the query differences. To consider the query difference in training, one can use a query-dependent loss function. To further consider the query difference in the test process, a

細(xì)節(jié)

Semi-supervised Rankinge number of unlabeled documents or queries at a low cost. It would be very helpful if one can leverage such unlabeled data in the learning-to-rank process. In this chapter, we mainly review a transductive approach and an inductive approach to this task, and discuss how to improve these approaches by

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Applications of Learning to Rankithm to solve a real ranking problem. In particular, we will take question answering, multimedia retrieval, text summarization, online advertising, etc. as examples, for illustration. One will see from these examples that the key step is to extract effective features for the objects to be ranked by

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cts of identity and identity construction of learners, teachers, and practitioners of science.Reports on empirical studies and commentaries serve to extend theoretical understandings related to identity and identity development vis-à-vis science education, link them to empirical evidence derived fro