Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer

Abstract

In order to solve the user cold-start problem caused by data-sparse in recommender system,this paper proposes a cross-domain recommendation algorithm based on aspect-level user preference transfer,named CAUT.CAUT is devised to learn aspect transfer across domains from a two-stage generative adversarial network and extract aspect-level user fine-grained prefe-rence from reviews.The data distribution misalignment between source and target domains is eliminated by fixing source domain encoder parameters and designing a domain discriminator.Then the user cold-start problem caused by data-sparse in the target domain could be alleviated by utilizing source domain data via CAUT.Experiments on real-world datasets show that the proposed CAUT outperforms SOTA models significantly in rating prediction RMSE indicator,suggesting that CAUT can effectively solve the user cold-start problem.