Hybrid Variational Autoencoder for Collaborative Filtering

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by...

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Vydáno v:2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) s. 251 - 256
Hlavní autoři: Liu, Jinxin, Xiao, Yingyuan, Zhu, Ke, Zheng, Wenguang, Hsu, Ching-Hsien
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 04.05.2022
Témata:
Autoencoder
Bayes methods
Collaborative filtering
Collaborative work
Computer crashes
Conferences
Decoding
Deep Learning
Manifolds
Rating Prediction
Recommendation System
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Shrnutí:In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance.
DOI:10.1109/CSCWD54268.2022.9776247