论文标题
基于用户资料和协作过滤的酒店推荐系统
Hotel Recommendation System Based on User Profiles and Collaborative Filtering
论文作者
论文摘要
如今，人们开始使用在线预订系统来计划假期，因为他们有很多可用的选择。从这个大规模选项中选择何时何地进行越来越困难。此外，由于在线预订系统上可以找到大量信息，有时消费者会错过更好的选择。从这个意义上讲，诸如推荐系统之类的个性化服务在决策中起着至关重要的作用。两种传统的推荐技术是基于内容和协作过滤的。尽管两种方法都有其优势，但它们也具有某些缺点，其中一些可以通过结合两种技术来提高建议质量来解决这些缺点。所得系统被称为混合推荐系统。本文提出了一种新的混合酒店推荐系统，该系统是通过结合基于内容和协作的过滤方法来开发的，该方法推荐客户所需的酒店并将其避免时间损失。
Nowadays, people start to use online reservation systems to plan their vacations since they have vast amount of choices available. Selecting when and where to go from this large-scale options is getting harder. In addition, sometimes consumers can miss the better options due to the wealth of information to be found on the online reservation systems. In this sense, personalized services such as recommender systems play a crucial role in decision making. Two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system. This paper presents a new hybrid hotel recommendation system that has been developed by combining content-based and collaborative filtering approaches that recommends customer the hotel they need and save them from time loss.
