摘要
在基于物品协同过滤的基础上,对隐式反馈数据进行挖掘建模,设计了隐式反馈偏好评分规则,并据此赋予了计算酒店相似度公式的新定义。考虑到用户的基本特征也会对用户个性化需求产生影响以及单一算法的局限性,进一步引入了XGBoost模型,利用XGBoost训练对改进后的推荐结果进行过滤,得到较好的个性化酒店推荐系统。文中采用真实的脱敏数据,证明利用层叠模型构建个性化酒店推荐系统的推荐效果更加精准,对于酒店在线平台的个性化服务具有较强的参考价值。
Based on collaborative filtering of items,this paper mines and models the implicit feedback data,designs the implicit feedback preference scoring rules,and gives a new definition to calculate the hotel similarity formula.At the same time,considering the basic characteristics of users will also have an impact on users?personalized needs and the limitations of a single algorithm,this paper further introduces the XGBoost model,and filters the improved recommendation results with XGBoost training,so as to obtain a better personalized hotel recommendation system.This paper adopts real desensitization data to prove that the recommendation effect of building personalized hotel recommendation system based on cascade model is more accurate,which has a strong reference value for the personalized service of online hotel platform.
作者
史达
于淼川
李梦琪
SHI Da;YU Miao-chuan;LI Meng-qi(School of Tourism and Hospitality Management,Dongbei University of Finance and Economics,Dalian 116025,Liaoning,China;International Business College,Dongbei University of Finance and Economics,Dalian 116025,Liaoning,China)
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2021年第7期1-10,共10页
Journal of Shandong University(Natural Science)
关键词
个性化酒店推荐
协同过滤
隐式反馈偏好设计
XGBoost模型
层叠模型
personalized hotel recommendation
collaborative filtering
implicit feedback preference design
XGBoost model
cascading model
