Estimating Probabilities in Recommendation Systems

Mingxuan Sun, Guy Lebanon, Paul Kidwell
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:734-742, 2011.

Abstract

Modeling ranked data is an essential component in a number of important applications including recommendation systems and web-search. In many cases, judges omit preference among unobserved items and between unobserved and observed items. This case of analyzing incomplete rankings is very important from a practical perspective and yet has not been fully studied due to considerable computational difficulties. We show how to avoid such computational difficulties and efficiently construct a non-parametric model for rankings with missing items. We demonstrate our approach and show how it applies in the context of collaborative filtering.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-sun11a, title = {Estimating Probabilities in Recommendation Systems}, author = {Sun, Mingxuan and Lebanon, Guy and Kidwell, Paul}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {734--742}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/sun11a/sun11a.pdf}, url = {https://proceedings.mlr.press/v15/sun11a.html}, abstract = {Modeling ranked data is an essential component in a number of important applications including recommendation systems and web-search. In many cases, judges omit preference among unobserved items and between unobserved and observed items. This case of analyzing incomplete rankings is very important from a practical perspective and yet has not been fully studied due to considerable computational difficulties. We show how to avoid such computational difficulties and efficiently construct a non-parametric model for rankings with missing items. We demonstrate our approach and show how it applies in the context of collaborative filtering.} }
Endnote
%0 Conference Paper %T Estimating Probabilities in Recommendation Systems %A Mingxuan Sun %A Guy Lebanon %A Paul Kidwell %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-sun11a %I PMLR %P 734--742 %U https://proceedings.mlr.press/v15/sun11a.html %V 15 %X Modeling ranked data is an essential component in a number of important applications including recommendation systems and web-search. In many cases, judges omit preference among unobserved items and between unobserved and observed items. This case of analyzing incomplete rankings is very important from a practical perspective and yet has not been fully studied due to considerable computational difficulties. We show how to avoid such computational difficulties and efficiently construct a non-parametric model for rankings with missing items. We demonstrate our approach and show how it applies in the context of collaborative filtering.
RIS
TY - CPAPER TI - Estimating Probabilities in Recommendation Systems AU - Mingxuan Sun AU - Guy Lebanon AU - Paul Kidwell BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-sun11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 734 EP - 742 L1 - http://proceedings.mlr.press/v15/sun11a/sun11a.pdf UR - https://proceedings.mlr.press/v15/sun11a.html AB - Modeling ranked data is an essential component in a number of important applications including recommendation systems and web-search. In many cases, judges omit preference among unobserved items and between unobserved and observed items. This case of analyzing incomplete rankings is very important from a practical perspective and yet has not been fully studied due to considerable computational difficulties. We show how to avoid such computational difficulties and efficiently construct a non-parametric model for rankings with missing items. We demonstrate our approach and show how it applies in the context of collaborative filtering. ER -
APA
Sun, M., Lebanon, G. & Kidwell, P.. (2011). Estimating Probabilities in Recommendation Systems. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:734-742 Available from https://proceedings.mlr.press/v15/sun11a.html.

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