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» Explaining collaborative filtering recommendations
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WWW
2005
ACM
14 years 8 months ago
Finding group shilling in recommendation system
In the age of information explosion, recommendation system has been proved effective to cope with information overload in ecommerce area. However, unscrupulous producers shill the...
Xue-Feng Su, Hua-Jun Zeng, Zheng Chen
SIGIR
2005
ACM
14 years 1 months ago
An industrial-strength content-based music recommendation system
We present a metadata free system for the interaction with massive collections of music, the MusicSurfer. MusicSurfer automatically extracts descriptions related to instrumentatio...
Pedro Cano, Markus Koppenberger, Nicolas Wack
DSS
2006
138views more  DSS 2006»
13 years 7 months ago
Design of a shopbot and recommender system for bundle purchases
The increasing proliferation of online shopping and purchasing has naturally led to a growth in the popularity of comparisonshopping search engines, popularly known as "shopb...
Robert S. Garfinkel, Ram D. Gopal, Arvind K. Tripa...
ML
2008
ACM
146views Machine Learning» more  ML 2008»
13 years 7 months ago
Improving maximum margin matrix factorization
Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
Markus Weimer, Alexandros Karatzoglou, Alex J. Smo...
ELECTRONICMARKETS
2000
130views more  ELECTRONICMARKETS 2000»
13 years 7 months ago
Virtual Communities of Transaction: The Role of Personalization in Electronic Commerce
Bringing communities of buyers and sellers together in the arena of electronic commerce stimulates three major potentials: the building of trust, the collection and effective use ...
Petra Schubert, Mark Ginsburg