RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

Abstract

Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to generate a Generalized User Representation (GUR) incorporating user information across domains in sequential recommendation, even when there is minimum or no common users in the two domains. We propose a self-attentive autoencoder to derive latent user representations, and a domain discriminator, which aims to predict the origin domain of a generated latent representation. We propose a novel adversarial learning method to train the two modules to unify user embeddings generated from different domains into a single global GUR for each user. The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved. Extensive experiments have been conducted on two public cross-domain recommendation datasets as well as a large dataset collected from real-world applications. The results demonstrate that RecGURU boosts performance and outperforms various state-of-the-art sequential recommendation and cross-domain recommendation methods. The collected data will be released to facilitate future research.

11 pages, 2 figures, 4 tables, Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

References in corpus (6)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Cross-lingual Language Model Pretraining
• Translation-based Recommendation
• CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
• Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
• CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users

Cited by in corpus (7)

• Contrastive Cross-Domain Sequential Recommendation
• One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
• A Comprehensive Survey on Self-Supervised Learning for Recommendation
• Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
• ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
• Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation
• Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users