RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User

• Main Authors: Yaru Jin, Shoubin Dong, Yong Cai, Jinlong Hu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Cross-domain recommendation; select reviews; fully-cold-start; review aware recommendation
• Online Access: https://ieeexplore.ieee.org/document/9042271/

Traditional recommendation algorithms such as matrix factorization, collaborative filtering perform poorly when lack of interactive information of user and product, known as the user cold-start problem, which may cut down the revenue of E-Commerce platform. Moreover, it is more challenging to generate recommendation lists for users who have no information at all because there is no preference information about them that could be leveraged, which is the user fully-cold-start problem. In this paper, a review aware cross-domain recommendation algorithm, called RACRec, is proposed to address the fully-cold-start problem in the field of product recommendation. Firstly, reviews are dynamically selected by using the adjacency matrix. Secondly, domain-specific preference vectors and domain-shared preference vectors of the cold start user are extracted by a migration model. On the other hand, the product feature vector in the target domain, which is generated from review texts by encoder and decoder, is combined with preference vectors of the cold-start user to make the rating prediction. Experiments on the Amazon datasets reveal that RACRec outperforms the state-of-the-art recommendation algorithms.