Research on Web Service Recommendation Algorithm Based on Hadoop Cloud Platform

As the modern internet technology rapidly evolves, an ocean of diverse online service data has opened up a rich and extensive range of choices for the vast user base. However, this scenario also ushers in the issue of information overload. Navigating the vast expanse of data to efficiently and accurately recommend personalized services has become a hot topic in the realm of academic research. The web service recommendation algorithm developed on the Hadoop cloud platform, by leveraging Hadoop's formidable distributed computing capabilities, has demonstrated unique and significant advantages in processing and analyzing massive data information. This paper will delve into the key technologies related to service recommendation systems, including content-based recommendations, collaborative filtering, and hybrid recommendations, and will integrate some core components of Hadoop, such as HDFS, MapReduce, and HBase, to conduct an in-depth analysis of implementing and designing an excellent service recommendation system in a cloud computing environment. The experimental results indicate that the recommendation algorithm developed based on Hadoop shows remarkable superiority in both processing efficiency and the quality of the final push, providing an effective solution to the challenges encountered in large-scale data processing.