Dyadic Prediction Using a Latent Feature Log-Linear Model

Details Dyadic Prediction Using a Latent Feature Log-Linear Model Dyadic Prediction Using a Latent Feature Log-Linear Model

2010-06-10

arxiv.org/abs/1006.2156v1

Computer Science

Learning

Scientific paper

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 the first model for dyadic prediction that satisfies 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) it is resistant to sample-selection bias, (v) it can learn well-calibrated probabilities, and (vi) it can scale to very large datasets. To our knowledge, no existing method satisfies all the above criteria. In particular, many methods assume that the labels are ordinal and ignore side-information when it is present. Experimental results show that the new method is competitive with state-of-the-art methods for the special cases of collaborative filtering and link prediction, and that it makes accurate predictions on nominal data.

Affiliated with

Also associated with

No associations

Rating

Dyadic Prediction Using a Latent Feature Log-Linear Model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Dyadic Prediction Using a Latent Feature Log-Linear Model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Dyadic Prediction Using a Latent Feature Log-Linear Model will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-76560