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ESWS
2008
Springer
13 years 11 months ago
Semantic Reasoning: A Path to New Possibilities of Personalization
Abstract. Recommender systems face up to current information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based...
Yolanda Blanco-Fernández, José J. Pa...
INTERACT
2003
13 years 11 months ago
Confidence Displays and Training in Recommender Systems
: Recommender systems help users sort through vast quantities of information. Sometimes, however, users do not know if they can trust the recommendations they receive. Adding a con...
Sean M. McNee, Shyong K. Lam, Catherine Guetzlaff,...
KDD
2006
ACM
170views Data Mining» more  KDD 2006»
14 years 10 months ago
Classification features for attack detection in collaborative recommender systems
Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in r...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R...
DBSOCIAL
2011
300views Database» more  DBSOCIAL 2011»
13 years 1 months ago
Boosting video popularity through recommendation systems
While search engines are the major sources of content discovery on online content providers and e-commerce sites, their capability is limited since textual descriptions cannot ful...
Renjie Zhou, Samamon Khemmarat, Lixin Gao, Huiqian...
WWW
2011
ACM
13 years 5 months ago
Ranking in context-aware recommender systems
As context is acknowledged as an important factor that can affect users’ preferences, many researchers have worked on improving the quality of recommender systems by utilizing ...
Minsuk Kahng, Sangkeun Lee, Sang-goo Lee