Conditional Ranking on Relational Data
Abstract
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. Conditional ranking from symmetric or reciprocal relations can in this framework be treated as two important special cases. Furthermore, we propose an efficient algorithm for conditional ranking by optimizing a squared ranking loss function. Experiments on synthetic and real-world data illustrate that such an approach delivers state-of-the-art performance in terms of predictive power and computational complexity. Moreover, we also show empirically that incorporating domain knowledge in the model about the underlying relations can improve the generalization performance.
Cite
Text
Pahikkala et al. "Conditional Ranking on Relational Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15883-4_32Markdown
[Pahikkala et al. "Conditional Ranking on Relational Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/pahikkala2010ecmlpkdd-conditional/) doi:10.1007/978-3-642-15883-4_32BibTeX
@inproceedings{pahikkala2010ecmlpkdd-conditional,
title = {{Conditional Ranking on Relational Data}},
author = {Pahikkala, Tapio and Waegeman, Willem and Airola, Antti and Salakoski, Tapio and De Baets, Bernard},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {499-514},
doi = {10.1007/978-3-642-15883-4_32},
url = {https://mlanthology.org/ecmlpkdd/2010/pahikkala2010ecmlpkdd-conditional/}
}