Tailoring recommendations for a multi-domain environment

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)42-45
Number of pages4
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2017
Event1st Workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning, RecSysKTL 2017 - Como, Italy
Duration: 27 Aug 2017 → …


  • Customizing recommendation approaches
  • Heterogeneous data
  • Multi-domain recommendation
  • Recommender systems

ASJC Scopus subject areas

  • Computer Science(all)


Dive into the research topics of 'Tailoring recommendations for a multi-domain environment'. Together they form a unique fingerprint.

Cite this