Collaborative Ranking for Local Preferences
Abstract
For many collaborative ranking tasks, we have access to relative preferences among subsets of items, but not to global preferences among all items. To address this, we introduce a matrix factorization framework called Collaborative Local Ranking (CLR). We justify CLR by proving a bound on its generalization error, the first such bound for collaborative ranking that we know of. We then derive a simple alternating minimization algorithm and prove that it converges in sublinear time. Lastly, we apply CLR to a novel venue recommendation task and demonstrate that it outperforms state-of-the-art collaborative ranking methods on real-world data sets.
Cite
Text
Kapicioglu et al. "Collaborative Ranking for Local Preferences." International Conference on Artificial Intelligence and Statistics, 2014.Markdown
[Kapicioglu et al. "Collaborative Ranking for Local Preferences." International Conference on Artificial Intelligence and Statistics, 2014.](https://mlanthology.org/aistats/2014/kapicioglu2014aistats-collaborative/)BibTeX
@inproceedings{kapicioglu2014aistats-collaborative,
title = {{Collaborative Ranking for Local Preferences}},
author = {Kapicioglu, Berk and Rosenberg, David S. and Schapire, Robert E. and Jebara, Tony},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2014},
pages = {466-474},
url = {https://mlanthology.org/aistats/2014/kapicioglu2014aistats-collaborative/}
}