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Improving maximum margin matrix factorization (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Weimer, Markus Smola, Alex Karatzoglou, Ros |
| Abstract | Maximum Margin Matrix Factorization (MMMF) has been proposed as a learning approach to the task of collaborative filtering with promising results. In our recent paper [2], we proposed to extend the general MMMF framework to allow for structured (ranking) losses in addition to the squared error loss. In this paper, we introduce a novel algorithm to compute the ordinal regression ranking loss which is significantly faster than the state of the art. In addition, we propose severals extensions to the MMMF model: We introduce offset terms to cater for user and item biases. Users exhibit vastly different rating frequencies ranging from only one rating per user to thousands of them. Similarly, some items get thousands of ratings while others get rated only once. We introduce an adaptive regularizer to allow for more complex models for those items and users with many ratings. Finally, we show equivalence between a recent extension introduced in [3] and a graph kernel approach described in [4]. Both aim at providing meaningful predictions for users with very little training data by virtue of the recommender graph. |
| File Format | |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Mmmf Model Collaborative Filtering General Mmmf Framework Item Bias Meaningful Prediction Severals Extension Novel Algorithm Recent Extension Little Training Data Recent Paper Many Rating Graph Kernel Approach Ordinal Regression Ranking Loss Adaptive Regularizer Error Loss Recommender Graph Different Rating Frequency Complex Model Maximum Margin Matrix Factorization |
| Content Type | Text |
