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A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Antti Airola; Tapio Pahikkala; Tero Aittokallio; Willem Waegeman; Bernard De Baets; Michiel Stock

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Antti Airola
Tapio Pahikkala
Tero Aittokallio
Willem Waegeman
Bernard De Baets
Michiel Stock
Katso/Avaa
paperBis.pdf (439.6Kb)
Lataukset: 

doi:10.1007/978-3-662-44851-9_33
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021042715307
Tiivistelmä


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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