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SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
6 years 10 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
WSDM
2012
ACM
246views Data Mining» more  WSDM 2012»
7 years 4 months ago
Auralist: introducing serendipity into music recommendation
Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, produc...
Yuan Cao Zhang, Diarmuid Ó Séaghdha,...
JCDL
2011
ACM
191views Education» more  JCDL 2011»
7 years 11 months ago
Serendipitous recommendation for scholarly papers considering relations among researchers
Serendipity occurs when one finds an interesting discovery while searching for something else. In digital libraries, recommendation engines are particularly well-suited for seren...
Kazunari Sugiyama, Min-Yen Kan
IPM
2011
91views more  IPM 2011»
7 years 11 months ago
Measuring the interestingness of articles in a limited user environment
Abstract-Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to ...
Raymond K. Pon, Alfonso F. Cardenas, David Buttler...
CORR
2011
Springer
166views Education» more  CORR 2011»
8 years 5 days ago
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help ...
Mehmet H. Göker, Pat Langley, Cynthia A. Thom...
KAIS
2011
102views more  KAIS 2011»
8 years 3 months ago
Symbolic data analysis tools for recommendation systems
Recommendation Systems have become an important tool to cope with the information overload problem by acquiring data about the user behavior. After tracing the user behavior, throu...
Byron Leite Dantas Bezerra, Francisco de Assis Ten...
CORR
2010
Springer
118views Education» more  CORR 2010»
8 years 5 months ago
Estimating Probabilities in Recommendation Systems
Modeling ranked data is an essential component in a number of important applications including recommendation systems and websearch. In many cases, judges omit preference among un...
Mingxuan Sun, Guy Lebanon, Paul Kidwell
SEMWEB
2010
Springer
8 years 6 months ago
dbrec - Music Recommendations Using DBpedia
Abstract. This paper describes the theoretical background and the implementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more ...
Alexandre Passant
PAAPP
2007
109views more  PAAPP 2007»
8 years 8 months ago
Relation rule mining
\Web users are nowadays confronted with the huge variety of available information sources whose content is not targeted at any specific group or layer. Recommendation systems aim...
Mehdi Adda, Rokia Missaoui, Petko Valtchev
ECRA
2007
139views more  ECRA 2007»
8 years 8 months ago
Common structure and properties of filtering systems
Recommendation systems have been studied actively since the 1990s. Generally, recommendation systems choose one or more candidates from a set of candidates through a filtering pro...
Junichi Iijima, Sho Ho
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