Tailoring Recommendations for a Multi-Domain Environment

Recommender systems are acknowledged as an essential instrument to support users in finding relevant information. However, the adaptation of recommender systems to multiple domain-specific requirements and data models still remains an open challenge. In the present paper, we contribute to this sparse line of research with guidance on how to design a customizable recommender system that accounts for multiple domains with heterogeneous data. Using concrete showcase examples, we demonstrate how to setup a multi-domain system on the item and system level, and we report evaluation results for the domains of (i) LastFM, (ii) FourSquare, and (iii) MovieLens. We believe that our findings and guidelines can support developers and researchers of recommender systems to easily adapt and deploy a recommender system in distributed environments, as well as to develop and evaluate algorithms suited for multi-domain settings.