Learning Disentangled User Representation Based on Controllable VAE for Recommendation
User behaviour on purchasing is always driven by complex latent factors, which are highly disentangled in the real world. Learning latent factorized representation of users can uncover user intentions behind the observed data (i.e. user-item interaction) and improve the robustness and interpretabili...
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Published in | Database Systems for Advanced Applications pp. 179 - 194 |
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Main Authors | , , , , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | User behaviour on purchasing is always driven by complex latent factors, which are highly disentangled in the real world. Learning latent factorized representation of users can uncover user intentions behind the observed data (i.e. user-item interaction) and improve the robustness and interpretability of the recommender system. However, existing collaborative filtering methods learning disentangled representation face problems of balancing the trade-off between reconstruction quality and disentanglement. In this paper, we propose a controllable variational autoencoder framework for collaborative filtering. Specifically, we adopt a modified Proportional-Integral-Derivative (PID) control to the β\documentclass[12pt]{minimal}
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\begin{document}$$\beta $$\end{document}-VAE objective to automatically tune the hyperparameter β\documentclass[12pt]{minimal}
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\begin{document}$$\beta $$\end{document} using the output of Kullback-Leibler divergence as feedback. We further introduce item embeddings to guide the system to learn representation related to the real-world concepts using a factorized Gaussian distribution. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines. We further evaluate our model’s effectiveness to control the trade-off between reconstruction error and disentanglement quality in the recommendation. |
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ISBN: | 3030731995 9783030731991 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-030-73200-4_12 |