Heterogeneous Graph Representation Learning for multi-target Cross-Domain Recommendation

Abstract

This paper discusses the current challenges in modeling real world recommendation scenarios and proposes the development of a unified Heterogeneous Graph Representation Learning framework for multi-target Cross-Domain recommendation (HGRL4CDR). A shared graph with user-item interactions from multiple domains is proposed as a way to provide an effective representation learning layer and unify the modelling of various heterogeneous data. A heterogeneous graph transformer network will be integrated to the representation learning model to prioritize the most important neighbours, and the proposed model would be able to capture complex information as well as adapt to dynamic changes in the data using matrix perturbation. Using the real world Amazon Review dataset, experiments would be conducted on multi-target cross domain recommendation.