The document discusses using trust-based recommender algorithms to improve recommendations on social platforms. It proposes using a T-index approach which models user trust relationships as a graph and calculates trust scores. An experiment tests this approach against collaborative filtering on four datasets, finding T-index achieves higher F1 scores and creates a more balanced trust network structure. The conclusions are that trust-based recommendations can improve performance for sparse data and support users in finding relevant content or connections on social networks.