Optimizing the design of recommendation systems

Xiaohan Guo, Dejun Xie*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


With the development of digital technology and internet applications, the massive market data and information overload make it hard for users to organize and make good use of comments to choose their preferred items. In this connection, recommendation systems are widely developed with high expectations. While there have been substantial researches focusing on the recommendation methods and implementations, this paper provides an up-to-date synthesis of the essential factors towards a more rigorous, user-helping system design in consolidation of collaborative filtering, content-based model, and hybrid model in a fast-developing web and social network environment. Aspects of evaluation metrics are analyzed and compared in connection to particular applicabilities in the system design. Further explorations in terms of credit rating are discussed in a real-business setting.

Original languageEnglish
Title of host publicationICCCM 2021 - Proceedings of the 9th International Conference on Computer and Communications Management
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450390071
Publication statusPublished - 2021
Event9th International Conference on Computer and Communications Management, ICCCM 2021 - Virtual, Online, Singapore
Duration: 16 Jul 202118 Jul 2021

Publication series

NameACM International Conference Proceeding Series


Conference9th International Conference on Computer and Communications Management, ICCCM 2021
CityVirtual, Online


  • Credit rating
  • Factor analysis
  • Recommendation system
  • System evaluation
  • User based model

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