Two-way grouping by one-way topic models
Eerika Savia, Kai Puolamäki and Samuel Kaski
In: IDA 2009, 31 Aug - 02 Sep 2009, Lyon, France.
We tackle the problem of new users or documents in collaborative
filtering. Generalization over users by grouping them into user groups
is beneficial when a rating is to be predicted for a relatively new
document having only few observed ratings.
The same applies for documents in the case of new users. We have
shown earlier that if there are both new users and new documents,
two-way generalization becomes necessary, and introduced a
probabilistic Two-Way Model for the task.
The task of finding a two-way grouping is a non-trivial combinatorial
problem, which makes it computationally difficult.
We suggest approximating the Two-Way Model with two URP models;
one that groups users and one that groups documents. Their
two predictions are combined using a product of experts model.
This combination of two one-way models achieves even better
prediction performance than the original Two-Way Model.