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» Methods and metrics for cold-start recommendations
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WEBI
2010
Springer
13 years 2 months ago
Reducing the Cold-Start Problem in Content Recommendation through Opinion Classification
Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The perform...
Damien Poirier, Françoise Fessant, Isabelle...
PAKDD
2010
ACM
172views Data Mining» more  PAKDD 2010»
13 years 9 months ago
Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems
Tag recommender systems are often used in social tagging systems, a popular family of Web 2.0 applications, to assist users in the tagging process. But in cold-start situations i.e...
Christine Preisach, Leandro Balby Marinho, Lars Sc...
ICDM
2010
IEEE
172views Data Mining» more  ICDM 2010»
13 years 2 months ago
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such ca...
Zeno Gantner, Lucas Drumond, Christoph Freudenthal...
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
KDD
2009
ACM
162views Data Mining» more  KDD 2009»
14 years 5 months ago
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations ...
Mohsen Jamali, Martin Ester