In the era of big data, the amount of Internet data is growing explosively.
How to quickly obtain valuable information from massive data has become a
challenging task. To effectively solve the problems faced by recommendation
technology, such as data sparsity, scalability, and real-time
recommendation, a personalized recommendation algorithm for e-commerce based
on Hadoop is designed aiming at the problems in collaborative filtering
recommendation algorithm. Hadoop cloud computing platform has powerful
computing and storage capabilities, which are used to improve the
collaborative filtering recommendation algorithm based on project, and
establish a comprehensive evaluation system. The effectiveness of the
proposed personalized recommendation algorithm is further verified through
the analysis and comparison with some traditional collaborative filtering
algorithms. The experimental results show that the e-commerce system based
on cloud computing technology effectively improves the support of various
recommendation algorithms in the system environment; the algorithm has good
scalability and recommendation efficiency in the distributed cluster, and
the recommendation accuracy is also improved, which can improve the
sparsity, scalability and real-time problems in e-commerce personalized
recommendation. This study greatly improves the recommendation performance
of e-commerce, effectively solves the shortcomings of the current
recommendation algorithm, and further promotes the personalized development
of e-commerce.
An Improved Collaborative Filtering Recommendation Algorithm for Big Data
