CofiRank or – shorter – cofi is our approach to the collaborative filtering problem. The goal is to predict preferences of users based on past ratings by them and other users. We build upon the Maximum Margin Matrix Factorization approach, yet extend it in several ways:
Cofi can make use of state of the art optimization technology, making it feasible to run on the largest data sets available. Cofi can be run on a single machine with moderate memory requirements (2GB) to train on the Netflix dataset with its 100.000.000 entries.
Cofi is able to do structured prediction, e.g. by predicting the relative order (ranking) with which you like movies instead of the absolute rating you would give them. This allows for models that are better suited to predict what you like than dislike. This property is important for recommender systems.
- Changes to previous version:
Initial Announcement on mloss.org.
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