Bridging Recommendations Across Domains: An Overview of Cross-Domain Recommendation

As the Internet has become increasingly ubiquitous and information has experienced explosive growth, recommendation systems have evolved to become an indispensable component across various fields. Traditional recommendation systems typically rely on either user historical preferences or item similarity for generating suggestions. However, cross-domain recommendation systems transcend these traditional boundaries by harnessing not only user historical preferences but also their behavioral data across different domains, significantly enhancing recommendation precision and personalization. This paper aims to provide a comprehensive exploration of cross-domain recommendation systems, including their core concepts, diverse application scenarios, underlying algorithm models, and the evaluation metrics used to gauge their effectiveness. Additionally, we’ll offer insights into potential future research directions. In an age where demand for more accurate and personalized recommendations continues to surge in our data-driven world, cross-domain recommendation systems are poised to play a pivotal role in shaping the future of information and content consumption.