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KDD
2005
ACM
218views Data Mining» more  KDD 2005»
14 years 11 months ago
A maximum entropy web recommendation system: combining collaborative and content features
Web users display their preferences implicitly by navigating through a sequence of pages or by providing numeric ratings to some items. Web usage mining techniques are used to ext...
Xin Jin, Yanzan Zhou, Bamshad Mobasher
OHS
2001
Springer
14 years 3 months ago
METIOREW: An Objective Oriented Content Based and Collaborative Recommending System
The size of Internet has been growing very fast and many documents appear every day in the Net. Users find many problems to obtain the information that they really need. In order t...
David Bueno, Ricardo Conejo, Amos David
TSMC
2008
109views more  TSMC 2008»
13 years 10 months ago
Providing Justifications in Recommender Systems
Abstract--Recommender systems are gaining widespread acceptance in e-commerce applications to confront the "information overload" problem. Providing justification to a re...
Panagiotis Symeonidis, Alexandros Nanopoulos, Yann...
WWW
2010
ACM
14 years 2 months ago
Diversifying landmark image search results by learning interested views from community photos
In this paper, we demonstrate a novel landmark photo search and browsing system, Agate, which ranks landmark image search results considering their relevance, diversity and qualit...
Yuheng Ren, Mo Yu, Xin-Jing Wang, Lei Zhang, Wei-Y...
UAI
2003
14 years 6 days ago
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Collaborative filtering (CF) and contentbased filtering (CBF) have widely been used in information filtering applications, both approaches having their individual strengths and...
Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying...