Hybrid Travel Recommendation Algorithm based on Center Aggregation Parameters

The fusion of content-based recommendation and collaborative filtering is an important research direction in recommendation algorithms. However, due to the scalability of collaborative filtering in processing data, the final results are often inaccurate. This article proposes a hybrid tourism recommendation algorithm based on central clustering parameters. Firstly, improve the K-means algorithm, secondly, improve the collaborative filtering process, and finally, perform weighted fusion of the two algorithms. The experimental results showed that the scalability issue of collaborative filtering was improved, and the accuracy of the hybrid recommendation algorithm was also improved.