AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information
Hsiang-Fu Yu, Hsin-Yuan Huang, Inderjit Dhillon, Chih-Jen Lin

Last modified: 2017-02-13


In many applications such as recommender systems and multi-label learning the task is to complete a partially observed binary matrix. Such PU learning (positive-unlabeled) problems can be solved by one-class matrix factorization (MF). In practice side information such as user or item features in recommender systems are often available besides the observed positive user-item connections. In this work we consider a generalization of one-class MF so that two types of side information are incorporated and a general convex loss function can be used. The resulting optimization problem is very challenging, but we derive an efficient and effective alternating minimization procedure. Experiments on large-scale multi-label learning and one-class recommender systems demonstrate the effectiveness of our proposed approach.


Matrix Factorization; PU Learning; Multi-label Learning

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