自动化学报2017，Vol.43Issue(9)：1597-1606,10.DOI:10.16383/j.aas.2017.c160644
基于矩阵填充和物品可预测性的协同过滤算法
Collaborative Filtering Recommendation Algorithm Based on Rating Matrix Filling and Item Predictability
摘要
Abstract
The traditional matrix filling algorithm ignores the difference between true rating and predictive rating,and there is only one standard on the traditional Top-N recommended method.In order to solve these two problems,an improved collaborative filtering algorithm is proposed.Firstly,the confidence coefficient is used to distinguish the credibility of the ratings.Then,a concept of item predictability is proposed.The program recommends items by comprehensively considering the item's predictive ratings and the predictability,and transforming the program into the 0-1 knapsack problem so as to select the optimized recommended list.Experimental results show that the algorithm can effectively alleviate the effect of sparsity and improve the performance of the recommendation,and that the optimization algorithm has good expansibility.关键词
协同过滤/推荐系统/预测评分/相似度/0-1背包问题Key words
Collaborative filtering/recommendation system/predictive ratings/similarity/0-1 knapsack problem引用本文复制引用
潘涛涛,文峰,刘勤让..基于矩阵填充和物品可预测性的协同过滤算法[J].自动化学报,2017,43(9):1597-1606,10.基金项目
国家高技术研究发展计划(863计划)(2014AA01A),国家自然科学基金(61572520)资助 Supported by National High Technology Research and Development Program (863 Program) (2014AA01A),National Natural Science Foundation of China (61572520) （863计划）
