基于增强学习的旅行计划推荐系统

Recommender System for Travel Plan Based on Reinforcement Learning

目前旅游和观光事业通过推荐系统帮助用户进行互动式对话获得目标。已有的推荐系统尽管互动性已经增强,但仍采用互动策略,在设计阶段需要指定先验。针对该问题,提出一个普遍适用的模型,基于增强学习技术设计一种旅行会话推荐系统,描述推荐系统采用的方法,总结一些关键问题。分析结果表明,该系统可自动学习自适应交互策略,

Tourism uses recommender systems to help user acquire their goals by using interactive dialogue. Although present recommender systems＇ interactivity has been enhanced, they still use interactive method. This paper presents a universally applicable model, design a conversation recommender system by using reinforcement learning technique which can learn adaptive interactive method automatically. Describe methods recommender systems use and summer up a number of important issues. Results testify the system＇s application in Austrian tourism portal.