Constraining embedding learning with Self-Matrix Factorization

Aldo Galeano, Ruben Jimenez, Suzana de Siqueira Santos, Alberto Paccanaro

Keywords: representation learning, constrained matrix decomposition, link prediction

TL;DR: We propose Self-Matrix Factorization (SMF), a method that learns object representations by constraining them with object similarities that are learned together with the representations from solely association data

Abstract:

We focus on the problem of learning object representations from solely association data, that is observed associations between objects of two different types, e.g. movies rated by users. We aim to obtain embeddings encoding object attributes that were not part of the learning process, e.g. movie genres. It has been shown that meaningful representations can be obtained by constraining the learning with manually curated object similarities. We propose Self-Matrix Factorization (SMF), a method that learns object representations and object similarities from observed associations, with the latter constraining the learned representations. In our extensive evaluation across three real-world datasets, we compared SMF with SLIM, HCCF and NMF obtaining better performance at predicting missing associations as measured by RMSE and precision at top-K. We also show that SMF outperforms the competitors at encoding object attributes as measured by the embedding distances between objects divided into attribute-driven groups.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 11390