基于协同过滤算法的旅游推荐系统的设计

Design of Travel Recommendation System Based on Collaborative Filtering Algorithm

随着互联网技术的迅速发展和信息资源的日益丰富,旅游行业的信息化程度越来越高,各个旅游景点的相关信息在各类系统中存储的数据量越来越大,用户查找自己感兴趣的旅游景区信息越来越困难。为了能够有效解决过载问题并向用户精准提供有效信息,设计了基于协同过滤算法的旅游推荐系统。系统利用爬虫技术在旅游景区官网和主流旅游网站中获取旅游相关旅游数据信息,通过协同过滤算法将相关的数据生成相应的推荐结果,可以直观形象地推荐给用户。

With the rapid development of Internet technology and the increasing abundance of information resources, the informationization of the tourism industry is becoming more and more advanced, and the amount of data related to each tourist attraction stored in various systems is getting larger and larger, making it more and more difficult for users to find information about tourist attractions that interest them. In order to effectively solve the overload problem and provide users with accurate and effective information, a travel recommendation system based on collaborative filtering algorithm is designed. The system uses crawler technology to obtain travel-related tourism data information in the official websites of tourist attractions and mainstream tourism websites, and generates the corresponding recommendation results through collaborative filtering algorithm, which can be intuitively and graphically recommended to users.