摘要
在推荐系统中,因评分尺度差异而造成的偏差问题一直影响着协同过滤算法的预测准确性。其中针对矩阵因子分解算法中的偏差问题,提出一种基于高阶偏差的因子分解机算法。该算法首先按照评分偏差的现实特征对用户和项目进行划分,再将偏差类别作为辅助特征集成到因子分解机中,实现了评分预测中不同偏差用户、项目的高阶交互。在MovieLens数据集上的实验结果表明,相比传统矩阵因子分解算法,提出的算法具有更低的预测误差,体现了其更好的推荐性能。
In recommender system, bias problem caused by different rating scales has always effected the predict precision of collaborative filtering. Concerning this bias problem of matrix factorization, this paper proposed a high-order biased factorization machine recommender algorithm. Firstly, it grouped users and items by their rating bias feature from real world, then integrated them into the factorization machine, which provided the high-order interactions between the different biased users and items. The experimental results on MovieLens datasets demonstrate that the proposed algorithm has lower prediction error than other traditional matrix factorization algorithms, which shows its better recommender performance.
出处
《计算机应用研究》
CSCD
北大核心
2017年第2期339-342,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(11201290
61104042)
关键词
推荐系统
矩阵因子分解
因子分解机
评分偏差
recommender system
matrix factorization
factorization machine
rating bias
