Abstract
Traditionally, single-domain recommender systems (SDRS) can suggest
suitable products for users to alleviate information overload. Nonetheless, cross-domain recommender systems (CDRS) have enhanced SDRS by accomplishing
specific objectives, such as improving precision and diversity and solving cold-start
and sparsity issues. Rather than considering each domain separately, CDRS uses
information gathered from a particular domain (e.g., music) to enhance
recommendations for another domain (e.g., films). Context-aware Recommender
System (CARS) focuses on optimizing the quality of suggestions, which are more
appropriate for users depending on their context. Integrating these techniques is helpful
for many cases where knowledge from several sources can be used to enhance
recommendations and where relevant contextual information is considered. This work
describes the main challenges and solutions of the state-of-the-art in Cross-Domain
Context-Aware Recommender Systems (CD-CARS), taking into account the
abundance of data on different domains and the systematic adoption of contextual data.
CD-CARS have shown efficient methods to tackle realistic recommendation scenarios,
preserving the benefits of CDRS (regarding cold-start and sparsity issues) and CARS
(assuming accuracy). Therefore, CD-CARS may direct future research to recommender
systems that use contextual information from multiple domains in a systematic way.