Heterogeneous Attention Concentration Link Prediction Algorithm for Attracting Customer Flow in Online Brand Community

Attracting users from a mature large online product community to a new small one by friend recommendation is vital for new product marketing in social network. However, the traditional link prediction algorithms for friend recommendation cannot get high accuracy because of the network sparsity and scale-free problems when attracting customer flow between large and small circles. In order to better adapt to the link prediction of node pairs between circles of different sizes, we propose a collaborative combined link prediction algorithm (CCLPA), which can deeply extract user attention concentration (AC) features in sparse networks. CCLPA possesses three distinctive merits. Firstly, different edges in the network are assigned different attention, and heterogeneous attention concentration indexes (HACIs) within and beyond triadic closure structure are defined accordingly. Second, a random forest (RF) model is designed to adaptively select the appropriate HACIs for a given circle structure, so as to avoid the impact of scale-free problem on link prediction accuracy between different circles. Third, according to the collaboration of the selected indexes and their sensitivity to the circle structure, appropriate sensitive collaborative heterogeneous attention concentration index (SCHACI) is built to avoid the negative impact of blind combination of indexes on predicted performance. Experimental results on Twitter confirm the effectiveness of our proposed method in attracting customer flow in online brand community.