摘要
针对现有的基于用户显式反馈信息的推荐系统推荐准确率不高的问题,提出了一种基于显式与隐式反馈信息的概率矩阵分解推荐方法。该方法综合考虑了显示反馈信息和隐式反馈信息,在对用户信任关系矩阵和商品评分矩阵进行概率分解的同时加入了用户评分记录的隐式反馈信息,优化训练模型参数,为用户提供精确的预测评分。实验结果表明,该方法可以有效地获得用户偏好,产生大量的准确度高的推荐。
Focusing on the issue that the recommender systems with explicit feedback drastically degrade the accuracy, the recommender technique using probabilistic matrix faetorization with explicit and implicit feedback was proposed. So the explicit and implicit feedback was taken into account in this method. Firstly, user trust relationship matrix and user- item matrix were factorized using probabilistic matrix faetorization to mix the feedback of user rating records. Then the model was trained to provide users with accurate prediction. The experimental results show that this technique can obtain user preferences effectively and produce large amounts of highly accurate recommendations.
出处
《计算机应用》
CSCD
北大核心
2015年第9期2574-2578,2601,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(60905040)
中国博士后科学基金资助项目(2013M531393)
江苏省"六大人才高峰"第十一批高层次人才选拔培养资助项目(XXRJ-009)
江苏省自然科学基金资助项目(BK20131382)
江苏省博士后科研资助计划项目(1102102C)
江苏省2014年度普通高校研究生实践创新计划项目(SJZZ_0107)
关键词
推荐系统
概率矩阵分解
显示反馈
隐式反馈
recommender system
probabilistic matrix factorization
explicit feedback
implicit feedback