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 Results 41 - 47 of 47 [Previous]  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
DE 2007-07-02
16:45		 Miyagi Akiu hot springs (Sendai) A Proposal of Context-Aware Ranking Method for Information Recommendation
Kenta Oku, Shinsuke Nakajima, Jun Miyazaki (NAIST), Shunsuke Uemura (Nara Sangyo Univ.), Hirokazu Kato (NAIST) DE2007-42		 In this paper, we propose a context-aware result ranking method for use in our SVM based context-aware restaurant recomm... [more] DE2007-42
pp.121-126
DE 2007-07-03
10:20		 Miyagi Akiu hot springs (Sendai) Interactive Re-ranking of Web Search Results by Social Bookmarks
Yusuke Yanbe, Satoshi Nakamura, Adam Jatowt, Katsumi Tanaka (Kyoto Univ.) DE2007-65		 ocial bookmarking services are an emerging type of Web services that let users store, share, annotate, and discover inte... [more] DE2007-65
pp.259-264
DE 2007-07-03
11:20		 Miyagi Akiu hot springs (Sendai) On Ranking Method for Search Results of XML Information Retrieval System Considering Nesting
Toshiyuki Shimizu, Masatoshi Yoshikawa (Kyoto Univ.) DE2007-67		 XML information retrieval systems search for relevant document fragments in XML documents for input queries. In general,... [more] DE2007-67
pp.271-275
DE 2007-07-03
15:50		 Miyagi Akiu hot springs (Sendai) Quantitative Evaluation and Feature Analysis of Search Engine Rankings
Yasuaki Yoshida, Takanori Ueda, Takashi Tashiro, Yu Hirate (Waseda Univ.), Hayato Yamana (Waseda Univ., NII) DE2007-95		 Most people use search engines in order to retrieve documents on the web. In this way, the social influence of search en... [more] DE2007-95
pp.441-446
AI 2007-05-31
11:35		 Tokyo Kikai-Shinko-Kaikan Bldg. Personalized Recommendation by Identifying Innovator
Noriaki Kawamae, Takeshi Yamada, Naonori Ueda (NTT) AI2007-4		 (To be available after the conference date) [more] AI2007-4
pp.19-24
PRMU, HIP 2006-01-20
16:55		 Osaka Osaka Univ. Unsupervised and Semi-Supervised Extraction of Fuzzy Clusters in Similarity Data
Weiwei Du, Kohei Inoue, Kiichi Urahama (Kyushu Univ.) 		A method is presented for sequential extraction of arbitrarily-shaped fuzzy clusters from similarity data. Clusters buri... [more] PRMU2005-177 HIP2005-147
pp.165-170
COMP 2005-09-15
16:35		 Osaka Osaka Univ., Toyonaka Campus How Easy to Learn Linear Ranking Functions
Atsuyoshi Nakamura (Hokkaido Univ.) 		In this paper, we study how easy to learn the class of {\em linear ranking functions}
from $n$-dimensional Euclidean sp... [more]		 COMP2005-35
pp.49-53
 Results 41 - 47 of 47 [Previous]  /   
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