Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Supervised probabilistic principal component analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Maximum Margin Matrix Factorization
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Dynamic Network Model for Predicting Occurrences of Salmonella at Food Facilities
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging social media networks for classification
Data Mining and Knowledge Discovery
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Probabilistic topic models for sequence data
Machine Learning
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In dyadic prediction, the input consists of a pair of items (a dyad), and the goal is to predict the value of an observation related to the dyad. Special cases of dyadic prediction include collaborative filtering, where the goal is to predict ratings associated with (user, movie) pairs, and link prediction, where the goal is to predict the presence or absence of an edge between two nodes in a graph. In this paper, we study the problem of predicting labels associated with dyad members. Special cases of this problem include predicting characteristics of users in a collaborative filtering scenario, and predicting the label of a node in a graph, which is a task sometimes called within-network classification or relational learning. This paper shows how to extend a recent dyadic prediction method to predict labels for nodes and labels for edges simultaneously. The new method learns latent features within a log-linear model in a supervised way, to maximize predictive accuracy for both dyad observations and item labels. We compare the new approach to existing methods for within-network classification, both experimentally and analytically. The experiments show, surprisingly, that learning latent features in an unsupervised way is superior for some applications to learning them in a supervised way.