  Based on the user-item bipartite network, collaborative filtering (CF)
recommender systems predict users' interests according to their history
collections, which is a promising way to solve the information exploration
problem. However, CF algorithm encounters cold start and sparsity problems. The
trust-based CF algorithm is implemented by collecting the users' trust
statements, which is time-consuming and must use users' private friendship
information. In this paper, we present a novel measurement to calculate users'
implicit trust-based correlation by taking into account their average ratings,
rating ranges, and the number of common rated items. By applying the similar
idea to the items, a item-based CF algorithm is constructed. The simulation
results on three benchmark data sets show that the performances of both
user-based and item-based algorithms could be enhanced greatly. Finally, a
hybrid algorithm is constructed by integrating the user-based and item-based
algorithms, the simulation results indicate that hybrid algorithm outperforms
the state-of-the-art methods. Specifically, it can not only provide more
accurate recommendations, but also alleviate the cold start problem.
