Collaborative Ranking for Local Preferences
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:466-474, 2014.
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.