i$^2$VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation

TL;DR

i$^2$VAE introduces a variational autoencoder framework with mutual information regularizers to enhance cross-domain sequential recommendation, especially for cold-start and long-tailed users, outperforming existing methods.

Contribution

The paper proposes i$^2$VAE, a novel VAE-based model with interest augmentation techniques that effectively improve recommendations for cold-start and long-tailed users in CDSR.

Findings

Outperforms state-of-the-art CDSR methods in experiments.

Enhances recommendation accuracy for cold-start users.

Effectively models long-tailed user interactions.

Abstract

Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across multiple domains to mitigate data sparsity and cold-start challenges in Single-Domain Sequential Recommendation. Existing methods primarily rely on shared users (overlapping users) to learn transferable interest representations. However, these approaches have limited information propagation, benefiting mainly overlapping users and those with rich interaction histories while neglecting non-overlapping (cold-start) and long-tailed users, who constitute the majority in real-world scenarios. To address this issue, we propose i$^2$VAE, a novel variational autoencoder (VAE)-based framework that enhances user interest learning with mutual information-based regularizers. i$^2$VAE improves recommendations for cold-start and long-tailed users while maintaining strong performance across all user groups. Specifically, cross-domain and disentangling regularizers extract transferable features for cold-start users, while a pseudo-sequence generator synthesizes interactions for long-tailed users, refined by a denoising regularizer to filter noise and preserve meaningful interest signals. Extensive experiments demonstrate that i$^2$VAE outperforms state-of-the-art methods, underscoring its effectiveness in real-world CDSR applications.