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Trust networks can be divided into user-to-user and user-to-commodity trust subnetworks. In those networks, it can be distinguished two kinds of classical trust relationships such as functional trust and referral trust. In fact, there is a third invisible trust relationship which we call latent trust. We propose the discovery algorithm to find the latent trust and compute the trust value of user-to-commodity trust which can help customers to directly make the final purchase decisions. Experiment results with the extended Epinions’ dataset illustrate that this algorithm has a better performance than other similar algorithms. Introduction Trust, as a means of social interaction, has attracted significant research interest in recent years [1,2] . Guha [3] , Liben-Nowell [4] and Zhang [5] et al. proposed TP algorithm, Neighbour(CN) algorithm and SP(Statistical inference problem) algorithm. Their “propagation” concept and probabilistic trust propagation model have better performance than before. Bur its direct inference and calculation on the information of OSNs usually make the trust network too complicated to be obtained and difficult to be maintained. Its statistical process is subject to probabilistic randomization, and its performance results are poor. In addition, the earlier studies dealt with the unobserved trust in the network based on the user-to-user (u-u) trust type rather than user-tocommodity (u-c) ones. For example, Guha et al. [3] proposed u-u trust and distrust prediction algorithm which remains a cornerstone study in trust prediction literature [2] . In our opinion, two actors (i.e., a user and a commodity) and two trust relationships (i.e., user-touser (u-u) and user-to-commodity (u-c)) are involved in the e-commerce application field. They play important different roles for forming the trust network. u-u trust relationships are bidirectional, u-c trust relationships are unidirectional. User-to-commodity’s trust degree determines the user’s purchase decision. In order to find out the invisible trust and simplify the trust network, the key point is determining the u-c trust network and calculating the similarity, reputation, differences of users’ reputation. The rest of this paper is organized as follows. Section 2 reviews the related work and concepts in the literature. Section 3 presents our algorithms. Section 4 presents the design of an experiment on a real OSN dataset. Section 5 concludes this paper and suggests future work.

A Latent Trust Discovery Algorithm Based on Optimization of Trust Network