Recommender Searching Mechanism for Trust-Aware Recommender Systems in Internet of Things

Abstract

Intelligent things are widely connected in Internet of Things (IoT) to enable ubiquitous service access. This may cause heavy service redundant. The trust-aware recommender system (TARS) is therefore proposed for IoT to help users finding reliable services. One fundamental requirement of TARS is to efficiently find as many recommenders as possible for the active users. To achieve this, existing approaches of TARS choose to search the entire trust network, which have very high computational cost. Though the trust network is the scale-free network, we show via experiments that TARS cannot find satisfactory number of recommenders by directly applying the classical searching mechanism. In this paper, we propose an efficient searching mechanism, named S_Searching: based on the scale-freeness of trust networks, choosing the global highest-degree nodes to construct a Skeleton, and searching the recommenders via this Skeleton. Benefiting from the superior outdegrees of the nodes in the Skeleton, S_Searching can find the recommenders very efficiently. Experimental results show that S_Searching can find almost the same number of recommenders as that of conducting full search, which is much more than that of applying the classical searching mechanism in the scale-free network, while the computational complexity and cost is much less.