多重对级贝叶斯个性化排序算法

Multi-pair Bayesian personalized ranking

为解决隐式反馈推荐问题,贝叶斯个性化排序(BPR)模型已经成为最具有代表性的对级(Pairwise)排序算法之一.在BPR模型中,存在一个严格的偏序假设:相较于未标记的物品而言,用户更喜欢已经有过标记行为的物品.本文提出了一种多重对级贝叶斯个性化排序(MBPR)推荐算法来进一步提升用户对物品的偏好预测能力.首先,基于BPR模型的排序关系设计了一种改进的多重对级偏序假设.具体地,对于每一用户,本文提出将未标记的反馈集细分为潜在的负反馈集和不确定性反馈集,并基于改进的对级偏序假设,提出了一种新的多重对级排序的优化目标来学习用户与物品之间的相关性.为实现MBPR模型的采样任务,本文设计了一种自适应采样策略来为模型更新动态地选取训练样本.最后,在公开数据集上开展了仿真推荐实验,并与基线算法对比.实验结果表明,MBPR算法能够取得更好的推荐效果.

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.