Collaborative Ranking for Local Preferences

Berk Kapicioglu, David Rosenberg, Robert Schapire, Tony Jebara
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:466-474, 2014.

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 this Paper


BibTeX
@InProceedings{pmlr-v33-kapicioglu14, title = {{Collaborative Ranking for Local Preferences}}, author = {Berk Kapicioglu and David Rosenberg and Robert Schapire and Tony Jebara}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {466--474}, year = {2014}, editor = {Samuel Kaski and Jukka Corander}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/kapicioglu14.pdf}, url = {http://proceedings.mlr.press/v33/kapicioglu14.html}, 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.} }
Endnote
%0 Conference Paper %T Collaborative Ranking for Local Preferences %A Berk Kapicioglu %A David Rosenberg %A Robert Schapire %A Tony Jebara %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-kapicioglu14 %I PMLR %P 466--474 %U http://proceedings.mlr.press/v33/kapicioglu14.html %V 33 %X 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.
RIS
TY - CPAPER TI - Collaborative Ranking for Local Preferences AU - Berk Kapicioglu AU - David Rosenberg AU - Robert Schapire AU - Tony Jebara BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-kapicioglu14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 466 EP - 474 L1 - http://proceedings.mlr.press/v33/kapicioglu14.pdf UR - http://proceedings.mlr.press/v33/kapicioglu14.html AB - 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. ER -
APA
Kapicioglu, B., Rosenberg, D., Schapire, R. & Jebara, T.. (2014). Collaborative Ranking for Local Preferences. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:466-474 Available from http://proceedings.mlr.press/v33/kapicioglu14.html.

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