Application of Data Science and Machine Learning Algorithms in Intelligent Recommendation System

Yuxuan Wang

doi:10.62051/ijcsit.v5n1.23

Authors

Yuxuan Wang

DOI:

https://doi.org/10.62051/ijcsit.v5n1.23

Keywords:

Intelligent recommendation system, Data science, Machine learning, Deep learning, User profiling

Abstract

Intelligent recommender systems use data science and machine learning algorithms to significantly improve recommendation accuracy and personalization through preprocessing, feature engineering and user profile construction. The study proposes solutions for challenges such as data sparsity and cold start, and experimentally verifies the effectiveness of algorithms such as XGBoost. Meanwhile, the potential of deep learning models and integrated learning in recommendation systems is explored. The study shows that the integration of data science and machine learning algorithms strengthens the data processing and pattern recognition capabilities and promotes the optimization and innovation of recommendation algorithms.

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