基于用户隐式数据的个性化酒店推荐算法Symbol`@@

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。

Personalized hotel recommendation algorithm based on user implicit data

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.