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RECSYS
2009
ACM
13 years 11 months ago
Using a trust network to improve top-N recommendation
Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ra...
Mohsen Jamali, Martin Ester
SIGIR
2010
ACM
13 years 8 months ago
Temporal diversity in recommender systems
Collaborative Filtering (CF) algorithms, used to build webbased recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current eva...
Neal Lathia, Stephen Hailes, Licia Capra, Xavier A...
RECSYS
2009
ACM
13 years 11 months ago
Applying relevant set correlation clustering to multi-criteria recommender systems
This thesis investigates application of clustering to multi-criteria ratings as a method of improving the precision of top-N recommendations. With the advent of ecommerce sites th...
Nkechi Nnadi
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
13 years 11 months ago
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
AINA
2009
IEEE
13 years 10 months ago
Do Metrics Make Recommender Algorithms?
Recommender systems are used to suggest customized products to users. Most recommender algorithms create collaborative models by taking advantage of web user profiles. In the las...
Elica Campochiaro, Riccardo Casatta, Paolo Cremone...