Enhanced Recommendation Systems: A Survey on the Impact of Auxiliary Information

In the age of big data, recommendation systems have become a critical tool for helping users navigate overwhelming amount online information. Enhanced take this one step further, leveraging latest algorithms and data-driven insights to deliver highly personalized relevant recommendations. This research paper provides comprehensive overview recent progress in enhanced systems, cov- ering current state-of-the-art techniques discussing opportunities challenges practitioners face. The article explores range approaches, including deep learning hybrid models that integrate both user item presents essential concepts, methods, applications driving advancement systems. We recognize press- ing hurdles field as sparsity diversity, thereby focusing on intent- based exploit additional/auxiliary information by aggregating implicit feedback from user-item interactions. gone further compiling benchmarks field, enabling new researchers explore innovate at much more thoughtful faster pace.