Response prediction using collaborative filtering with hierarchies and side-information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Link prediction via matrix factorization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
Discovering latent factors from movies genres for enhanced recommendation
Proceedings of the sixth ACM conference on Recommender systems
Discovering social circles in ego networks
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
A Monte Carlo algorithm for cold start recommendation
Proceedings of the 23rd international conference on World wide web
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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.