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» Preference Networks: Probabilistic Models for Recommendation...
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SIGIR
2011
ACM
12 years 10 months ago
Collaborative competitive filtering: learning recommender using context of user choice
While a user’s preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learni...
Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hon...
AH
2006
Springer
13 years 11 months ago
eDAADe: An Adaptive Recommendation System for Comparison and Analysis of Architectural Precedents
We built a Web-based adaptive recommendation system for students to select and suggest architectural cases when they analyze "Case Study" work within the architectural de...
Shu-Feng Pan, Ji-Hyun Lee
IBPRIA
2003
Springer
14 years 21 days ago
A Probabilistic Model for the Cooperative Modular Neural Network
Abstract. This paper presents a model for the probability of correct classification for the Cooperative Modular Neural Network (CMNN). The model enables the estimation of the perf...
Luís A. Alexandre, Aurélio C. Campil...
SAC
2008
ACM
13 years 7 months ago
Whom should I trust?: the impact of key figures on cold start recommendations
Generating adequate recommendations for newcomers is a hard problem for a recommender system (RS) due to lack of detailed user profiles and social preference data. Empirical evide...
Patricia Victor, Chris Cornelis, Ankur Teredesai, ...
UAI
2003
13 years 8 months ago
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Collaborative filtering (CF) and contentbased filtering (CBF) have widely been used in information filtering applications, both approaches having their individual strengths and...
Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying...