Cross-Domain Transfer in AI-Powered Recommendation Systems
DOI:
https://doi.org/10.63345/sjaibt.v2.i3.110Keywords:
Cross-Domain Transfer, Recommendation Systems, Transfer Learning, Domain Adaptation, Neural Networks, Collaborative FilteringAbstract
Recommendation systems have become central to digital platforms, enabling personalized content delivery in domains ranging from e-commerce and entertainment to healthcare and education. Traditional recommendation models rely on abundant, domain-specific training data, which limits their ability to generalize across contexts. Cross-domain transfer in AI-powered recommendation systems addresses this challenge by leveraging knowledge from one domain and applying it to another, enabling more accurate, robust, and adaptable recommendations even in data-sparse environments.
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