完全冷启动下的个性化推荐算法
Personalized recommendation algorithm under completely cold start
李剑锋 1陈海龙 1翟军 1林岩1
作者信息
- 1. 大连海事大学航运经济与管理学院管理科学与工程系,辽宁大连 116026
- 折叠
摘要
为解决推荐算法中新物品完全冷启动问题,针对数据精准性不足和用户个性化缺失问题,提出一种完全冷启动个性化推荐算法.在运用过滤方法形成精准数据的基础上,引入个性化因子,改变原有物品的相似关联性,推荐依据会随着用户个性化特征而有所变动.经过对比分析,发现融入个性化的完全冷启动推荐算法仅查全率基本不变,精确率、假正率和F1值多个评价指标得到提升,此外,P-R曲线、ROC曲线以及提升曲线都说明该算法具有更好的推荐效果.
Abstract
To solve the problem of complete cold start on new items in recommendation algorithm,a com-plete cold start personalized recommendation algorithm was proposed in view of the lack of data accuracy and user personaliza-tion.With accurate data formed using filtering method,the personalized factor was introduced to adjust the original items'simi-larities,thus recommending basis change with the user's personalized characters.Through the analysis of comparison,the per-sonalized recommendation algorithm for completely cold start is found with the almost unchanged recall rate,as well as the im-proved precision rate,false positive rate and F1 value.In addition,P-R curve,ROC curve and depth-lift curve also show that the algorithm has better recommendation results.
关键词
推荐算法/完全冷启动/个性化推荐/近相邻算法/物品冷启动/过滤方法/数据精准性Key words
recommendation algorithm/completely cold start/personalized recommendation/neighbor algorithm/item cold start/filtering method/data accuracy引用本文复制引用
基金项目
国家自然科学基金项目(72271037)
中央高校基本科研业务费专项资金基金项目(3132019353)
出版年
2024
NETL
NSTL
万方数据