Connectionist learning of expert preferences by comparison training
Advances in neural information processing systems 1
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Preference Model for Structured Supervised Learning Tasks
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Label ranking by learning pairwise preferences
Artificial Intelligence
Journal of Artificial Intelligence Research
Complex objects ranking: a relational data mining approach
Proceedings of the 2010 ACM Symposium on Applied Computing
Learning to order: a relational approach
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Network regression with predictive clustering trees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed (i.i.d.). At the same time, it offers the unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. In this work, we propose a method for learning to rank from network data when data distribution may change over time. The learned models can be used to predict the ranking of nodes in the network for new time periods. The proposed method modifies the SVMRank algorithm in order to emphasize the importance of models learned in time periods during which data follow a data distribution that is similar to that observed in the new time period. We evaluate our approach on several real world problems of learning to rank from network data, coming from the area of sensor networks.