Trust Exploitation in Graph based Social Recommender Systems : A Survey

The increased usage of social networks in day-to-day life has paved the path for social recommender models. The research in social recommender has seen a multi-directional approach in the last decade, among which the graph neural network-based approaches are most sought. Since the graph-based approaches can efficiently replicate the intrinsic behavior of the social networks. Social networks work on the notion of relationships between users, which is generally known as trust in recommender. Trust is the centric part of the social recommender models. Trust exploitation differs from model to model. However, trust in most models is used at a directed or propagated level only. In this article, we first provide the dynamics of trust and its terminologies, followed by state-of-the-art social recommenders that exploit these trust relations using graph-based models followed by their comparative analysis. At last, we provide the open issues and promising research direction of social recommender and how trust can be further infused in recommender. This article is the first such survey of social recommenders focusing on trust usage.