Co-pairwise ranking model for item recommendation - Bin WU; Yun CHEN; Zhongchuan SUN; Yangdong YE (Tongxin xuebao)
Most of existing recommendation models constructed pairwise samples only from a user’s perspective.Nevertheless,they overlooked the functional relationships among items--A key factor that could significantly influence user purchase decision-making process.To this end,a co-pairwise ranking model was proposed,which modeled a user’s preference for a given item as the combination of user-item interactions and item-item complementarity relationships.Considering that the rank position of positive sample and the negative sampler had a direct impact on the rate of convergence,a rank-aware learning algorithm was devised for optimizing the proposed model.Extensive experiments on four real-word datasets are conducted to evaluate of the proposed model.The experimental results demonstrate that the devised algorithm significantly outperforms a series of state-of-the-art recommendation algorithms in terms of multiple evaluation metrics.