  Knowledge graph (KG) based Collaborative Filtering is an effective approach
to personalizing recommendation systems for relatively static domains such as
movies and books, by leveraging structured information from KG to enrich both
item and user representations. Motivated by the use of Transformers for
understanding rich text in content-based filtering recommender systems, we
propose Content-aware KG-enhanced Meta-preference Networks as a way to enhance
collaborative filtering recommendation based on both structured information
from KG as well as unstructured content features based on Transformer-empowered
content-based filtering. To achieve this, we employ a novel training scheme,
Cross-System Contrastive Learning, to address the inconsistency of the two very
different systems and propose a powerful collaborative filtering model and a
variant of the well-known NRMS system within this modeling framework. We also
contribute to public domain resources through the creation of a large-scale
movie-knowledge-graph dataset and an extension of the already public
Amazon-Book dataset through incorporation of text descriptions crawled from
external sources. We present experimental results showing that enhancing
collaborative filtering with Transformer-based features derived from
content-based filtering outperforms strong baseline systems, improving the
ability of knowledge-graph-based collaborative filtering systems to exploit
item content information.
