  Cold Start (CS) and sparse evaluation problems dramatically degrade
recommendation performance in large-scale recommendation systems such as Taobao
and eBay. We name this degradation as the sparse trust problem, which will
cause the decrease of the recommendation accuracy rate. To address this problem
we propose a novel sparse trust mining method, which is based on the Roundtable
Gossip Algorithm (RGA). First, we define the relevant representation of sparse
trust, which provides a research idea to solve the problem of sparse evidence
in the large-scale recommendation system. Based on which the RGA is proposed
for mining latent sparse trust relationships between entities in large-scale
recommendation systems. Second, we propose an efficient and simple
anti-sparsification method, which overcomes the disadvantages of random trust
relationship propagation and Grade Inflation caused by different users have
different standard for item rating. Finally, the experimental results show that
our method can effectively mine new trust relationships and mitigate the sparse
trust problem.
