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Collaborative prediction using ensembles of maximum margin matrix factorizations (2006)
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- [imls.engr.oregonstate.edu]
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| Venue: | In ICML |
| Citations: | 35 - 0 self |
BibTeX
@INPROCEEDINGS{Decoste06collaborativeprediction,
author = {Dennis Decoste},
title = {Collaborative prediction using ensembles of maximum margin matrix factorizations},
booktitle = {In ICML},
year = {2006},
pages = {249--256},
publisher = {ACM}
}
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Abstract
Fast gradient-based methods for Maximum Margin Matrix Factorization (MMMF) were recently shown to have great promise (Rennie & Srebro, 2005), including significantly outperforming the previous state-of-the-art methods on some standard collaborative prediction benchmarks (including MovieLens). In this paper, we investigate ways to further improve the performance of MMMF, by casting it within an ensemble approach. We explore and evaluate a variety of alternative ways to define such ensembles. We show that our resulting ensembles can perform significantly better than a single MMMF model, along multiple evaluation metrics. In fact, we find that ensembles of partially trained MMMF models can sometimes even give better predictions in total training time comparable to a single MMMF model. 1.
Keyphrases
maximum margin matrix factorization collaborative prediction single mmmf model standard collaborative prediction benchmark multiple evaluation metric alternative way total training time rennie srebro previous state-of-the-art method gradient-based method mmmf model great promise ensemble approach
