Abstract/Summary

Knowledge graphs (KGs) have been popularly used in recommender systems to leverage high-order connections between users and items. Typically, KGs are constructed based on semantic information derived from metadata. However, item images are also highly useful, especially for those domains where visual factors are influential such as fashion items. In this paper, we propose an approach to augment visual information extracted by popularly used image feature extraction methods into KGs. Specifically, we introduce visually-augmented KGs where the extracted information is integrated by using visual factor entities and visual relations. Moreover, to leverage the augmented KGs, a user representation learning approach is proposed to learn hybrid user profiles that combine both semantic and visual preferences. The proposed approaches have been applied in top-$N$ recommendation tasks on two real-world datasets. The results show that the augmented KGs and the representation learning approach can improve the recommendation performance. They also show that the augmented KGs are applicable in the state-of-the-art KG-based recommender system as well.