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STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 7 months ago
Probabilistic Association Rules for Item-Based Recommender Systems
Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users ...
Sylvain Castagnos, Armelle Brun, Anne Boyer
EDM
2009
116views Data Mining» more  EDM 2009»
13 years 3 months ago
Determining the Significance of Item Order In Randomized Problem Sets
Researchers who make tutoring systems would like to know which sequences of educational content lead to the most effective learning by their students. The majority of data collecte...
Zachary A. Pardos, Neil T. Heffernan
BTW
2011
Springer
218views Database» more  BTW 2011»
12 years 9 months ago
Tracking Hot-k Items over Web 2.0 Streams
Abstract: The rise of the Web 2.0 has made content publishing easier than ever. Yesterday’s passive consumers are now active users who generate and contribute new data to the web...
Parisa Haghani, Sebastian Michel, Karl Aberer
LREC
2008
121views Education» more  LREC 2008»
13 years 7 months ago
Automatic Acquisition for low frequency lexical items
This paper addresses a specific case of the task of lexical acquisition understood as the induction of information about the linguistic characteristics of lexical items on the bas...
Núria Bel, Sergio Espeja, Montserrat Marimo...
WWW
2009
ACM
14 years 10 days ago
Tagommenders: connecting users to items through tags
Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navig...
Shilad Sen, Jesse Vig, John Riedl