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.

中文翻译：

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基于用户画像和协同过滤的酒店推荐系统

如今，人们开始使用在线预订系统来计划他们的假期，因为他们有大量的选择。从这个大规模选项中选择何时何地去变得越来越困难。此外，有时消费者可能会因为在线预订系统上的大量信息而错过更好的选择。从这个意义上说，推荐系统等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同过滤。虽然这两种方法都有其优点，但它们也有一定的缺点，其中一些缺点可以通过结合这两种技术来提高推荐质量来解决。由此产生的系统被称为混合推荐系统。