Filter-enhanced Contrast Variational AutoEncoders for sequential recommendation
Zhijin Chen, Nankai Lin, Aimin Yang, Dong Ming Zhou · The Computer Journal · 2025
Abstract Data augmentation-based contrastive learning has been successfully employed in Variational AutoEncoders sequence recommendation systems to tackle the issue of data sparsity. Nevertheless, this strategy is generally less advantageous for tail users. The prospective transmission of information from head-to-tail users to alleviate long-tail impact is encouraging. However, data augmentation distorts the original sequence and embeds stochastic noise into latent variables, impeding the decoder’s capacity to accurately identify the user’s true preferences. In addition, contrastive learning seeks to achieve consistency in the latent variables of both the original and augmented data. However, the presence of noise in the augmented data might hamper the encoding of latent variables from the original data, especially impacting head users. In order to address these challenges, this work introduces a new sequence recommendation model called the Filter-enhanced Contrastive Variational Autoencoder (FeCVAE). It employs Fourier filters and adversarial attack training to minimize the impact of stochastic noise, thereby improving the quality of latent variables and facilitating more accurate decoder outputs. Moreover, a user enhancer is introduced to leverage knowledge from head users to empower tail users, thereby alleviating the long-tail effect. The efficacy of FeCVAE is demonstrated through comprehensive experiments across four benchmark datasets.