Planned intervention: On Thursday 19/09 between 05:30-06:30 (UTC), Zenodo will be unavailable because of a scheduled upgrade in our storage cluster.
Published September 19, 2023 | Version v1
Conference paper Open

Conditioning of variational autoencoder by user traits for item recommendation

  • 1. Denso IT Laboratory, Shibuya, Tokyo, Japan

Description

Deep learning-based recommendation algorithms have recently at- tracted attention due to their effectiveness at processing big data. Methods based on the variational autoencoder (VAE) are particularly promising thanks to their advantage with the data sparsity problem in recommendation tasks. However, because user traits affect the preference of recommended items, to improve the performance of VAE-based recommendation methods, it is necessary to carefully consider user traits. In this paper, we propose a method that conditions the VAE with user trait labels for switching the distributions of a generative model of latent variables. Experiments on a music recommendation task demonstrate that utilizing user trait labels estimated from tweet history leads to an improved performance and that the distribution can be changed depending on the individual traits of users.

Files

MuRS23-2-Tachioka.pdf

Files (203.8 kB)

Name Size Download all
md5:fc294126726999eb5a75341ce41e1431
203.8 kB Preview Download