TY - GEN
T1 - A Novel Collaborative Filtering Framework Based on Variational Self-Attention GAN
AU - Sun, Weifeng
AU - Yu, Shumiao
AU - Yang, Jin
AU - Dong, Boxiang
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - It is difficult for users to find the required information promptly in the massive data. The collaborative filtering is an effective way to help users get the proper information. To achieve better performance, an improved framework based on variational Generative Adversarial Networks with self-attention (VGCF) is proposed. In VGCF, self-attention mechanism and Variational Autoencoders are combined to form self-attention variational encoder, which improves the ability of acquiring explicit and implicit features of sparse data and obtains the personal preference and the correlation between users. By utilizing Generative Adversarial Networks for prediction, the generative model uses the compression matrix obtained by self-attention variational encoder to generate the predicted user-items interaction matrix. The discriminative model provides a better approximation for the posterior and maximum-likelihood assignment, which makes the generated result closer to the real data distribution. Finally, we show that the performance of VGCF is significantly better than the state-of-the-art recommendation methods on several real-world datasets.
AB - It is difficult for users to find the required information promptly in the massive data. The collaborative filtering is an effective way to help users get the proper information. To achieve better performance, an improved framework based on variational Generative Adversarial Networks with self-attention (VGCF) is proposed. In VGCF, self-attention mechanism and Variational Autoencoders are combined to form self-attention variational encoder, which improves the ability of acquiring explicit and implicit features of sparse data and obtains the personal preference and the correlation between users. By utilizing Generative Adversarial Networks for prediction, the generative model uses the compression matrix obtained by self-attention variational encoder to generate the predicted user-items interaction matrix. The discriminative model provides a better approximation for the posterior and maximum-likelihood assignment, which makes the generated result closer to the real data distribution. Finally, we show that the performance of VGCF is significantly better than the state-of-the-art recommendation methods on several real-world datasets.
KW - collaborative filtering
KW - generative adversarial networks
KW - self-attentions
KW - variational autoencoders
UR - http://www.scopus.com/inward/record.url?scp=85100382353&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9322300
DO - 10.1109/GLOBECOM42002.2020.9322300
M3 - Conference contribution
AN - SCOPUS:85100382353
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -