Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Nantonac collaborative filtering: recommendation based on order responses
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Order SVM: a kernel method for order learning based on generalized order statistics
Systems and Computers in Japan
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Supervised Ordering — An Empirical Survey
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On the Complexity of Learning Lexicographic Strategies
The Journal of Machine Learning Research
Linear feature-based models for information retrieval
Information Retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
FRank: a ranking method with fidelity loss
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Relaxing Ceteris Paribus Preferences with Partially Ordered Priorities
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Extending CP-nets with stronger conditional preference statements
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
The complexity of learning separable ceteris paribus preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Ceteris Paribus preference elicitation with predictive guarantees
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning conditional preference networks with queries
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning conditional preference networks
Artificial Intelligence
Adapting boosting for information retrieval measures
Information Retrieval
Aggregating preference ranking with fuzzy Data Envelopment Analysis
Knowledge-Based Systems
Democratic approximation of lexicographic preference models
Artificial Intelligence
Contracting preference relations for database applications
Artificial Intelligence
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UCP-networks: a directed graphical representation of conditional utilities
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
Towards a user based recommendation strategy for digital ecosystems
Knowledge-Based Systems
An empirical investigation of ceteris paribus learnability
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
The problem of learning Conditional Preference Networks (CP-nets) from a set of pairwise comparisons between outcomes has received great attention recently. However, because of the randomicity of the users' behaviors or the observation errors, there exists some noise (errors) in the training samples. Most existing methods neglect to handle the case with noisy samples. In this work, we introduce a new model of learning CP-nets from noisy samples. Based on chi-squared testing, we propose an algorithm to solve this problem in polynomial time. We prove that the obtained CP-net converges in mean to initial CP-net as sample size increases. The proposed method is verified on both simulated data and real data. Compared with the previous methods, our method achieves more accurate results on noisy sample sets.