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|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 its running time is independent of the number of training examples. 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.|
|Citation:||Kapicioglu, B, Rosenberg, DS, Schapire, RE, Jebara, T. (2014). Collaborative ranking for local preferences. Journal of Machine Learning Research, 33 (466 - 474|
|Pages:||466 - 474|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Journal of Machine Learning Research|
|Version:||Final published version. This is an open access article.|
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