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» Parallel learning to rank for information retrieval
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WWW
2008
ACM
14 years 8 months ago
Online learning from click data for sponsored search
Sponsored search is one of the enabling technologies for today's Web search engines. It corresponds to matching and showing ads related to the user query on the search engine...
Massimiliano Ciaramita, Vanessa Murdock, Vassilis ...
CIKM
2011
Springer
12 years 7 months ago
A probabilistic method for inferring preferences from clicks
Evaluating rankers using implicit feedback, such as clicks on documents in a result list, is an increasingly popular alternative to traditional evaluation methods based on explici...
Katja Hofmann, Shimon Whiteson, Maarten de Rijke
ICPP
2008
IEEE
14 years 2 months ago
Parallelization and Characterization of Probabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis (PLSA) is one of the most popular statistical techniques for the analysis of two-model and co-occurrence data. It has applications in inform...
Chuntao Hong, Wenguang Chen, Weimin Zheng, Jiulong...
IUI
2003
ACM
14 years 27 days ago
Adapting to the user's internet search strategy on small devices
World Wide Web search engines typically return thousands of results to the users. To avoid users browsing through the whole list of results, search engines use ranking algorithms ...
Jean-David Ruvini
KDD
2006
ACM
167views Data Mining» more  KDD 2006»
14 years 8 months ago
Identifying "best bet" web search results by mining past user behavior
The top web search result is crucial for user satisfaction with the web search experience. We argue that the importance of the relevance at the top position necessitates special h...
Eugene Agichtein, Zijian Zheng