Evaluating Content-based Pre-Training Strategies for a Knowledge-aware Recommender System based on Graph Neural Networks

In this paper, we introduce a Knowledge-aware Recommender System (KARS) based on Graph Neural Networks that exploit pre-trained content-based embeddings to improve the representation of users and items. Our approach relies on the intuition that textual features can describe the items in the catalog from a different point of view, so they are worth to be exploited to provide users with more accurate recommendations. Accordingly, we used encoding techniques to learn a pre-trained representation of the items in the catalogue based on textual content, and we used these embeddings to feed the input layer of a KARS based on GCNs. In this way, the GCN is able to encode both the knowledge coming from the unstructured content and the structured knowledge provided by the KG (ratings and item descriptive properties). As shown in our experiments, the exploitation of pre-trained embeddings improves the predictive accuracy of the KARS, which overcomes all the baselines we considered in several experimental settings.