Towards the next generation of multi-criteria recommender systems

Zhe Li*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


This paper presents the motivation, concepts, ideas and research questions underlying a PhD research project in the domain of recommender systems, and more specifically on multi-criteria recommendation. While we build on the existing work in this direction, we aim at introducing recommendation frameworks that do not only optimize for different criteria simultaneously, but also exploit their interrelations. For this aim, we will address three multi-criteria recommendation challenges, namely multi-modal user and item modeling, package recommendation, and user-centric recommendation. For realizing these frameworks, and in particular, for learning interactions and interrelations in the criteria space, we will rely on the state-of-the-art deep learning systems, and in particular the Generative Adversarial Networks (GANs). In addition, a novel evaluation strategy for multi-criteria recommendation targeting the maximization of the user's satisfaction will also be devised.

Original languageEnglish
Title of host publicationRecSys '18
Subtitle of host publicationProceedings of the 12th ACM Conference on Recommender Systems
Place of PublicationNew York, NY
PublisherAssociation for Computer Machinery
Number of pages5
ISBN (Print)978-1-4503-5901-6
Publication statusPublished - 2018
Event12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada
Duration: 2 Oct 20187 Oct 2018


Conference12th ACM Conference on Recommender Systems, RecSys 2018


  • Multi-criteria
  • Recommender Systems
  • User Modeling
  • User-centered Recommendation


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