中南民族大学学报（自然科学版）2018，Vol.37Issue(1)：114-119,6.
MOOB:一种改进的基于Bandit模型的推荐算法
MOOB: An Improved Recommendation Algorithm Based on Bandit Model
摘要
Abstract
A multi-objective optimization recommendation algorithm based on upper confidence bound algorithm is proposed. The algorithm can effectively avoid Matthew effect on the basis of ensuring the accuracy of prediction, and improve the mining ability of the recommendation system to the long tail items. YaHoo's news recommendation data set is used in experiments, experimental results show that the multi-objective optimization recommendation algorithm can effectively solve the problem of long-tail item excavation,avoid the Matthew effect and improve the precision and breadth of the recommended system under the condition of high prediction accuracy.关键词
Bandit模型/马太效应/长尾现象/多目标优化/覆盖率Key words
bandit model/Matthew effect/long tail phenomenon/multi-objective optimization/coverage分类
信息技术与安全科学引用本文复制引用
帖军,孙荣苑,孙翀,郑禄..MOOB:一种改进的基于Bandit模型的推荐算法[J].中南民族大学学报（自然科学版）,2018,37(1):114-119,6.基金项目
国家科技支撑计划项目子课题(2015BAD29B01),中央高校基本科研业务费专项资金项目(CZP17007) （2015BAD29B01）
