  Trust among the users of a social network plays a pivotal role in item
recommendation, particularly for the cold start users. Due to the sparse nature
of these networks, trust information between any two users may not be always
available. To infer the missing trust values, one well-known approach is path
based trust estimation, which suggests a user to believe all of its neighbors
in the network. In this context, we propose two threshold-based heuristics to
overcome the limitation of computation for the path based trust inference. It
uses the propagation phenomena of trust and decides a threshold value to select
a subset of users for trust propagation. While the first heuristic creates the
inferred network considering only the subset of users, the second one is able
to preserve the density of the inferred network coming from all users
selection. We implement the heuristics and analyze the inferred networks with
two real-world datasets. We observe that the proposed threshold based heuristic
can recover up to 70 \% of the paths with much less time compared to its
deterministic counterpart. We also show that the heuristic based inferred trust
is capable of preserving the recommendation accuracy.
