基于遗忘函数的均值贝叶斯个性化排序算法研究

针对贝叶斯个性化排序算法未能充分应用用户的行为信息,导致算法在数据稀疏情况下推荐性能以及鲁棒性均大幅度降低的问题,提出了均值贝叶斯个性化排序(MBPR)算法,来进一步挖掘用户对隐式反馈信息的偏好关系。考虑到用户兴趣随时间变化的特征,又将遗忘函数引入MBPR算法中。该算法首先对用户的历史评分记录进行预处理;然后根据用户的评分信息对项目进行正负反馈的划分,对每名用户进行个性化建模,挖掘用户对未参与项目的喜好程度,生成推荐列表。为验证提出算法的推荐性能,在公开数据集MovieLens及Yahoo上进行分析和对比实验。实验结果表明该算法的推荐性能及鲁棒性较对比算法均有显著提高。

Research on forgetting function-based mean Bayesian personalized ranking algorithm

To address the problem that the Bayesian personalized sorting algorithm fails to fully apply the user’s behavior information,which leads to a significant reduction in recommendation performance and robustness under sparse data conditions,this paper proposed the mean Bayesian personalized ranking(MBPR)algorithm to further explore the user’s preference for implicit feedback information.Considering the characteristics of user interest changing over time,the MBPR algorithm introduced the forgetting function.The algorithm firstly preprocessed the historical rating records of users,then divided the items into positive and negative feedback based on the rating information of users,personalized the modeling for each user,explored the users’preferences for unengaged items,and generated a list of recommendations.To verify the recommendation performance of the proposed algorithm,this paper conducted analysis and comparison experiments on the public data sets MovieLens and Yahoo.Experimental results show that the recommendation performance and robustness of the proposed algorithm are significantly improved over the comparison algorithm.