Abstract
Every recommender system needs the notion of preferences of a user to suggest one item and not another. However, current recommender algorithms deduct these preferences by first predicting an actual rating of the items and then sorting those. Departing from this, we present an algorithm that is capable of directly learning the preference function from given ratings. The presented approach combines recent results on preference learning, state-of-the-art optimization algorithms, and the large margin approach to capacity control. The algorithm follows the matrix factorization paradigm to collaborative filtering. Maximum Margin Matrix Factorization (MMMF) has been introduced to control the capacity of the prediction to avoid overfitting. We present an extension to this approach that is capable of using the methodology developed by the Learning to Rank community to learn a ranking of unrated items for each user. In addition, we integrate several recently proposed extensions to MMMF into one coherent framework where they can be combined in a mix-and-match fashion.
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Notes
- 1.
We assume \(\mathrm{argsort}(f)\) to sort f decreasingly.
- 2.
The implementation did not scale to the bigger data sets Eachmovie and Netflix.
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Acknowledgements
Markus Weimer has been funded under Grant 1223 by the German Science Foundation (DFG). Alexandros Karatzoglou was funded by a grant of the ANR - CADI project. We gratefully acknowledge support by the Frankfurt Center for Scientific Computing in running our experiments. We would also like to thank Alex Smola for important discussions and support.
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Karatzoglou, A., Weimer, M. (2010). Collaborative Preference Learning. In: Fürnkranz, J., Hüllermeier, E. (eds) Preference Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_19
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DOI: https://doi.org/10.1007/978-3-642-14125-6_19
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