  Personalized review-based rating prediction aims at leveraging existing
reviews to model user interests and item characteristics for rating prediction.
Most of the existing studies mainly encounter two issues. First, the rich
knowledge contained in the fine-grained aspects of each review and the
knowledge graph is rarely considered to complement the pure text for better
modeling user-item interactions. Second, the power of pre-trained language
models is not carefully studied for personalized review-based rating
prediction. To address these issues, we propose an approach named
Knowledge-aware Collaborative Filtering with Pre-trained Language Model
(KCF-PLM). For the first issue, to utilize rich knowledge, KCF-PLM develops a
transformer network to model the interactions of the extracted aspects w.r.t. a
user-item pair. For the second issue, to better represent users and items,
KCF-PLM takes all the historical reviews of a user or an item as input to
pre-trained language models. Moreover, KCF-PLM integrates the transformer
network and the pre-trained language models through representation propagation
on the knowledge graph and user-item guided attention of the aspect
representations. Thus KCF-PLM combines review text, aspect, knowledge graph,
and pre-trained language models together for review-based rating prediction. We
conduct comprehensive experiments on several public datasets, demonstrating the
effectiveness of KCF-PLM.
