A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
This work addresses a specific challenge in dyadic prediction for applications like recommender systems, but it is incremental as it builds on existing pairwise learning and spectral filtering ideas.
The paper tackles the full and almost full cold start problem in dyadic prediction, where both objects in a pair are new or rarely seen during training, by introducing a two-step learning algorithm that slightly improves predictive performance compared to existing tensor product kernel methods.
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad have not been observed during training, or if one of them has been observed, but very few times. A popular approach for addressing this problem is to train a model that makes predictions based on a pairwise feature representation of the dyads, or, in case of kernel methods, based on a tensor product pairwise kernel. As an alternative to such a kernel approach, we introduce a novel two-step learning algorithm that borrows ideas from the fields of pairwise learning and spectral filtering. We show theoretically that the two-step method is very closely related to the tensor product kernel approach, and experimentally that it yields a slightly better predictive performance. Moreover, unlike existing tensor product kernel methods, the two-step method allows closed-form solutions for training and parameter selection via cross-validation estimates both in the full and almost full cold start settings, making the approach much more efficient and straightforward to implement.