Towards Recommender Systems Integrating Contextual Information from Multiple Domains through Tensor Factorization

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.