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A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction

Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman, A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction. In: Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo (Eds.), Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), Lecture Notes in Computer Science 8725, 517–532 , Springer, 2014.

http://dx.doi.org/10.1007/978-3-662-44851-9_33

Abstract:

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.

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BibTeX entry:

@INPROCEEDINGS{inpPaStAiAiDeWa14a,
  title = {A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction},
  booktitle = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014)},
  author = {Pahikkala, Tapio and Stock, Michiel and Airola, Antti and Aittokallio, Tero and De Baets, Bernard and Waegeman, Willem},
  volume = {8725},
  series = {Lecture Notes in Computer Science},
  editor = {Calders, Toon and Esposito, Floriana and Hüllermeier, Eyke and Meo, Rosa},
  publisher = {Springer},
  pages = {517–532 },
  year = {2014},
  keywords = {Machine learning, kernel methods, tensor product, transfer learning, dyadic prediction, ridge regression},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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