Exploring Challenges and Innovations in E-Commerce Recommendation Systems: A Comprehensive Review
摘要
Recommendation systems play a pivotal role in the digital age, with ongoing research focused on enhancing their effectiveness. This paper delves into the common challenges associated with developing these systems, including the cold-start problem, handling sparse datasets, and the use of matrix filling in hierarchical methods. We explore innovative approaches that include the integration of diverse algorithms and the application of alternative techniques, such as deep learning. Our research aims to establish an empirically based standard for various aspects of recommendation systems, thereby serving as a valuable reference for future studies.