  Trust is a fundamental concept in many real-world applications such as
e-commerce and peer-to-peer networks. In these applications, users can generate
local opinions about the counterparts based on direct experiences, and these
opinions can then be aggregated to build trust among unknown users. The
mechanism to build new trust relationships based on existing ones is referred
to as trust inference. State-of-the-art trust inference approaches employ the
transitivity property of trust by propagating trust along connected users. In
this paper, we propose a novel trust inference model (MaTrust) by exploring an
equally important property of trust, i.e., the multi-aspect property. MaTrust
directly characterizes multiple latent factors for each trustor and trustee
from the locally-generated trust relationships. Furthermore, it can naturally
incorporate prior knowledge as specified factors. These factors in turn serve
as the basis to infer the unseen trustworthiness scores. Experimental
evaluations on real data sets show that the proposed MaTrust significantly
outperforms several benchmark trust inference models in both effectiveness and
efficiency.
