A Log-Linear Model with Latent Features for Dyadic Prediction

In dyadic prediction, labels must be predicted for pairs (dyads) whose members possess unique identifiers and, sometimes, additional features called side-information. Special cases of this problem include collaborative filtering and link prediction. We present a new log-linear model for dyadic prediction that is the first to satisfy several important desiderata: (i) labels may be ordinal or nominal, (ii) side-information can be easily exploited if present, (iii) with or without side-information, latent features are inferred for dyad members, (iv) the model is resistant to sample-selection bias, (v) it can learn well-calibrated probabilities, and (vi) it can scale to large datasets. To our knowledge, no existing method satisfies all the above criteria. In particular, many methods assume that the labels are binary or numerical, and cannot use side-information. Experimental results show that the new method is competitive with previous specialized methods for collaborative filtering and link prediction. Other experimental results demonstrate that the new method succeeds for dyadic prediction tasks where previous methods cannot be used. In particular, the new method predicts nominal labels accurately, and by using side-information it solves the cold-start problem in collaborative filtering.