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

by   Chenglin Li, et al.

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.


page 1

page 2

page 3

page 4


One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation

Cross-domain recommendation is an important method to improve recommende...

Mixed Information Flow for Cross-domain Sequential Recommendations

Cross-domain sequential recommendation is the task of predict the next i...

Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation

Cross domain recommendation (CDR) has been proposed to tackle the data s...

DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation

Sequential Recommendation (SR) characterizes evolving patterns of user b...

RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems

Data sparsity and data imbalance are practical and challenging issues in...

Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of seq...

Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations

Cross domain recommender systems have been increasingly valuable for hel...

Please sign up or login with your details

Forgot password? Click here to reset