Skeleton Searching Strategy for Recommender Searching Mechanism of Trust-Aware Recommender Systems

摘要

A trust-aware recommender system (TARS) is widely used in social media to find useful information. S_Searching is one of the most effective recommender searching mechanisms of TARS. It is based on the scale-freeness of the trust network: a skeleton, which consists of hub nodes of the trust network, is involved in trust propagations. Trusts are first propagated from active users to the skeleton, and then recommenders are searched via the skeleton. One fundamental research issue in S_Searching is to search the skeleton for active users efficiently. Existing methods fully search the trust network to find hub nodes in the skeleton for active users. It has high computational cost. In this paper, we propose a novel iterative deepening-based skeleton searching strategy for S_Searching, in which a depth-limited search is run repeatedly. The depth limit is increased with each iteration until it reaches the maximum allowable trust propagation distance. Simulation results show that the computational complexity of our proposed strategy is much less expensive than that of existing methods.