To solve the implicit recommendation problems,Bayesian Personalized Ranking(BPR) algorithm has become one the most representative pairwise methods.Generally,BPR assumes that users keep higher preference on observed items than unobserved items.In this paper,we introduce Multi-pair Bayesian Personalized Ranking (MBPR),a novel pairwise method to further investigate the preference about the large number of unobserved feedbacks.First,we propose an enhanced pairwise assumption based on the traditional pairwise assumption adopted by BPR.Specifically,we divide the large unobserved item set into two parts:uncertain item set and possibly negative item set for each user.Based on this,a new multi-pair pairwise objective function is proposed to learn users' preference.To solve the sampling task in MBPR,an adaptive sampling strategy is then proposed to dynamically draw uncertain feedbacks from unobserved item set.Finally,empirical studies show that our algorithms can improve the ranking performance of BPR.