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» Maximum Margin Ranking Algorithms for Information Retrieval
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KDD
2009
ACM
245views Data Mining» more  KDD 2009»
14 years 8 months ago
Mining rich session context to improve web search
User browsing information, particularly their non-search related activity, reveals important contextual information on the preferences and the intent of web users. In this paper, ...
Guangyu Zhu, Gilad Mishne
WWW
2005
ACM
14 years 8 months ago
Identifying link farm spam pages
With the increasing importance of search in guiding today's web traffic, more and more effort has been spent to create search engine spam. Since link analysis is one of the m...
Baoning Wu, Brian D. Davison
JCDL
2010
ACM
187views Education» more  JCDL 2010»
13 years 9 months ago
Social network document ranking
In search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usual...
Liang Gou, Xiaolong Zhang, Hung-Hsuan Chen, Jung-H...
SIGIR
2009
ACM
14 years 2 months ago
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recomme...
Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen, Can ...
SIGIR
2002
ACM
13 years 7 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby