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- 197 hasAuthorList authorList.
- 197 isPartOf proceedings.
- 197 keyword "Poster Session".
- 197 title "Discovering User Profiles".
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- item-1 hasContent riddhiman-ghosh.
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- item-2 hasContent mohamed-dekhil.
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- 198 type InProceedings.
- 198 label "Bootstrapped Extraction of Class Attributes".
- 198 sameAs 198.
- 198 abstract "As an alternative to previous studies on extracting class attributes from unstructured text, which consider either Web documents or query logs as the source of textual data, A bootstrapped method extracts class attributes simultaneously from both sources, using a small set of seed attributes. The method improves extraction precision and also improves attribute relevance across 40 test classes.".
- 198 hasAuthorList authorList.
- 198 isPartOf proceedings.
- 198 keyword "Poster Session".
- 198 title "Bootstrapped Extraction of Class Attributes".
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- item-1 hasContent joseph-reisinger.
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- item-2 hasContent marius-pasca.
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- 2 type InProceedings.
- 2 label "Click Chain Model in Web Search".
- 2 sameAs 2.
- 2 abstract "Given a terabyte click log, can we build an efficient and effective click model? It is commonly believed that web search click logs are a gold mine for search business, because they reflect users' preference over web documents presented by the search engine. Click models provide a principled approach to inferring user-perceived relevance of web documents, which can be leveraged in numerous applications in search businesses. Due to the huge volume of click data, scalability is a must. We present the click chain model (CCM), which is based on a solid, Bayesian framework. It is both scalable and incremental, perfectly meeting the computational challenges imposed by the voluminous click logs that constantly grow. We conduct an extensive experimental study on a data set containing 8.8 million query sessions obtained in July 2008 from a commercial search engine. CCM consistently outperforms two state-of-the-art competitors in a number of metrics, with over 9.7% better log-likelihood, over 6.2% better click perplexity and much more robust (up to 30%) prediction of the first and the last clicked position.".
- 2 hasAuthorList authorList.
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- 2 keyword "Data Mining".
- 2 title "Click Chain Model in Web Search".
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- item-1 hasContent fan-guo.
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- item-2 hasContent chao-liu.
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- item-3 hasContent anitha-kannan.
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- item-4 hasContent tom-minka.
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- item-7 hasContent christos-faloutsos.
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- 20 type InProceedings.
- 20 label "Towards Context-Aware Search by Learning a Very Large Variable Length Hidden Markov Model from Search Logs".
- 20 sameAs 20.
- 20 abstract "Capturing the context of a user's query from the previous queries and clicks in the same session may help understand the user's information need. A context-aware approach to document re-ranking, query suggestion, and URL recommendation may improve users' search experience substantially. In this paper, we propose a general approach to context-aware search. To capture contexts of queries, we learn a variable length Hidden Markov Model (vlHMM) from search sessions extracted from log data. Although the mathematical model is intuitive, how to learn a large vlHMM with millions of states from hundreds of millions of search sessions poses a grand challenge. We develop a strategy for parameter initialization in vlHMM learning which can greatly reduce the number of parameters to be estimated in practice. We also devise a method for distributed vlHMM learning under the map-reduce model. We test our approach on a real data set consisting of 1.8 billion queries, 2.6 billion clicks, and 840 million search sessions, and evaluate the effectiveness of the vlHMM learned from the real data on three search applications: document re-ranking, query suggestion, and URL recommendation. The experimental results show that our approach is both effective and efficient.".
- 20 hasAuthorList authorList.
- 20 isPartOf proceedings.
- 20 keyword "Data Mining".
- 20 title "Towards Context-Aware Search by Learning a Very Large Variable Length Hidden Markov Model from Search Logs".
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- item-1 hasContent huanhuan-cao.
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- item-2 hasContent daxin-jiang.
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