Popularity-Aware Graph Social Recommendation for Fully Non-Interaction Users

In this paper, we address a novel social recommendation for users who have no interactions with items (unobserved users). This task can provide many applications such as recommendations for cold-start users after the first sign-up and targeted advertising, thus, it seems to be extremely meaningful. However, existing social recommendation methods are unsuitable for this task since they assume that all users have interactions with items or cannot recommend more effectively than MostPopular recommendation. Towards this end, we propose Unobserved user-oriented Graph Social Recommendation (UGSR), which learns the preferences of unobserved users and provides richer recommendations than MostPopular recommendation. The popularity-aware graph convolutional network, which is carefully designed for this task, simultaneously considers some user-item interactions, social relations, and item popularity for the effective user and item modeling.