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» Explaining collaborative filtering recommendations
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IIR
2010
13 years 9 months ago
Context-Dependent Recommendations with Items Splitting
Recommender systems are intelligent applications that help on-line users to tackle information overload by providing recommendations of relevant items. Collaborative Filtering (CF...
Linas Baltrunas, Francesco Ricci
KDD
2006
ACM
172views Data Mining» more  KDD 2006»
14 years 8 months ago
Attack detection in time series for recommender systems
Recent research has identified significant vulnerabilities in recommender systems. Shilling attacks, in which attackers introduce biased ratings in order to influence future recom...
Sheng Zhang, Amit Chakrabarti, James Ford, Fillia ...
EDM
2009
114views Data Mining» more  EDM 2009»
13 years 5 months ago
Edu-mining for Book Recommendation for Pupils
This paper proposes a novel method for recommending books to pupils based on a framework called Edu-mining. One of the properties of the proposed method is that it uses only loan h...
Ryo Nagata, Keigo Takeda, Koji Suda, Jun'ichi Kake...
SAC
2005
ACM
14 years 1 months ago
A trust-enhanced recommender system application: Moleskiing
Recommender Systems (RS) suggests to users items they will like based on their past opinions. Collaborative Filtering (CF) is the most used technique to assess user similarity bet...
Paolo Avesani, Paolo Massa, Roberto Tiella
CORR
2008
Springer
133views Education» more  CORR 2008»
13 years 8 months ago
Emergence of Spontaneous Order Through Neighborhood Formation in Peer-to-Peer Recommender Systems
The advent of the Semantic Web necessitates paradigm shifts away from centralized client/server architectures towards decentralization and peer-to-peer computation, making the exi...
Ernesto Diaz-Aviles, Lars Schmidt-Thieme, Cai-Nico...