Threshold-Based Heuristics for Trust Inference in a Social Network

Abstract

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.

Trust information network in social Internet of things using trust-aware recommender systems

“…42 Trust transitivity means that trust must be propagated. 46,47 Trust asymmetry is that the structure of a trust network should be a directional and weighted graph like in the real world. Trust dynamicity is that the level and the state of trust change over time.…”