Application of Travel Service Recommendation Algorithm Based on Cloud Computing

Abstract

In the context of big data, traditional collaborative filtering recommendation algorithms cannot provide users with accurate recommendation services, making the sparsity of user data become an important factor affecting the accuracy of recommendation in the complex social network environment. This paper mainly studies the design and application of travel service recommendation algorithm based on cloud computing. In this paper, the data is processed based on MapReduce parallel computing framework to improve the performance and speed of the algorithm. The Linux-equipped cluster is deployed under the Hadoop framework. The cluster feasibility test is carried out to realize the recommendation function of the algorithm, and the function of the algorithm is realized according to the actual problems. On the Hadoop big data platform, the traditional algorithm was improved and the hadoop-based collaborative filtering recommendation algorithm was implemented to further improve the recommendation rate and accuracy of the traditional recommendation filtering algorithm.

The Classification of Application Users Supporting and Facilitating Travel Mobility Using Two-Step Cluster Analysis

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