  Promoting information spreading is a booming research topic in network
science community. However, the exiting studies about promoting information
spreading seldom took into account the human memory, which plays an important
role in the spreading dynamics. In this paper we propose a non-Markovian
information spreading model on complex networks, in which every informed node
contacts a neighbor by using the memory of neighbor's accumulated contact
numbers in the past. We systematically study the information spreading dynamics
on uncorrelated configuration networks and a group of $22$ real-world networks,
and find an effective contact strategy of promoting information spreading,
i.e., the informed nodes preferentially contact neighbors with small number of
accumulated contacts. According to the effective contact strategy, the high
degree nodes are more likely to be chosen as the contacted neighbors in the
early stage of the spreading, while in the late stage of the dynamics, the
nodes with small degrees are preferentially contacted. We also propose a
mean-field theory to describe our model, which qualitatively agrees well with
the stochastic simulations on both artificial and real-world networks.
