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» Adapting information retrieval systems to user queries
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KDD
2009
ACM
152views Data Mining» more  KDD 2009»
16 years 7 months ago
TANGENT: a novel, 'Surprise me', recommendation algorithm
Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored ...
Kensuke Onuma, Hanghang Tong, Christos Faloutsos
MOBIDE
2005
ACM
16 years 9 days ago
Video-streaming for fast moving users in 3G mobile networks
The emergence of third-generation (3G) mobile networks offers new opportunities for the effective delivery of data with rich content including multimedia messaging and video-strea...
Anna Kyriakidou, Nikos Karelos, Alex Delis
CIKM
2005
Springer
16 years 8 days ago
Finding similar questions in large question and answer archives
There has recently been a significant increase in the number of community-based question and answer services on the Web where people answer other peoples’ questions. These serv...
Jiwoon Jeon, W. Bruce Croft, Joon Ho Lee
ACL
2007
15 years 8 months ago
Measuring Importance and Query Relevance in Topic-focused Multi-document Summarization
The increasing complexity of summarization systems makes it difficult to analyze exactly which modules make a difference in performance. We carried out a principled comparison be...
Surabhi Gupta, Ani Nenkova, Daniel Jurafsky
WWW
2005
ACM
16 years 7 months ago
Clustering for probabilistic model estimation for CF
Based on the type of collaborative objects, a collaborative filtering (CF) system falls into one of two categories: item-based CF and user-based CF. Clustering is the basic idea i...
Qing Li, Byeong Man Kim, Sung-Hyon Myaeng