Inherent-attribute-aware dual-graph autoencoder for rating prediction

Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users' preferences. However, existing methods still have two significant limitations: i) External attributes are often unavailable in the real world due to privacy issues, leading to low quality of representations; and ii) existing methods lack considering complex associations in users' rating behaviors during the encoding process. To meet these challenges, this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder, named IADGAE, for rating prediction. To address the low quality of representations due to the unavailability of external attributes, we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users' rating behaviors to strengthen user and item representations. To exploit the complex associations hidden in users’ rating behaviors, we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items. Moreover, we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph. Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods, which achieves a significant improvement of 4.51%∼41.63 ​% in the RMSE metric.