A cross-domain recommender system using deep coupled autoencoders

by   Alexandros Gkillas, et al.

Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items without sharing sensitive information regarding the users' behavior, and thus avoiding the leak of user privacy. In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations, thus transferring beneficial information from the source to the target domain. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the item-latent factors across domains. Extensive numerical experiments on two publicly available benchmark datasets are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.


page 1

page 4

page 6


A Deep Framework for Cross-Domain and Cross-System Recommendations

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR)...

Cross-domain Recommendation via Deep Domain Adaptation

The behavior of users in certain services could be a clue that can be us...

Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains

Prerequisite chain learning helps people acquire new knowledge efficient...

DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems

Click-Through Rate (CTR) prediction is one of the main tasks of the reco...

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

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

Recommending with Recommendations

Recommendation systems are a key modern application of machine learning,...

It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation

Linear autoencoder models learn an item-to-item weight matrix via convex...

Please sign up or login with your details

Forgot password? Click here to reset