A contextual Bayesian user experience model for scholarly recommender systems

Zohreh D. Champiri, Brian Fisher, Chun Yong Chong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

1 Citation (Scopus)


Since the advent of scholarly recommender systems (SRSs), more than 200 papers in the related area have been published. Many of these papers focus on proposing new and more accurate algorithms, or to enhance existing ones. Recently we have seen growing interest in embedding recommending methods into User Experience (UX), to enhance the value of RSs for users. Researchers have proposed that UX can be affected by bottlenecks in human perception, the preconceptions of the individual, and related factors such as personal and situational characteristics, which can be considered as contextual information. Although there are a few studies on developing User Models (UMs) in the field of SRSs, it has been emphasized that incorporating contextual information into user modelling and creating recommendations based on the users’ information needs can be an effective approach to personalization and better UX with SRSs. The aim of this paper is to operationalize relevant contexts and to design a Bayesian UM for assisting the diagnosis of scholars’ information needs in terms of accurate, novel, diverse, and popular research papers. The proposed user model can be embedded in the process of recommending and identifying the users’ information needs which help recommenders to retrieve more appropriate recommendations and consequently leads to the enhancement of the UX for SRSs. Finally, the robustness and performance of the proposed Bayesian UM are evaluated.

Original languageEnglish
Title of host publicationArtificial Intelligence in HCI - Second International Conference, AI-HCI 2021 Held as Part of the 23rd HCI International Conference, HCII 2021 Virtual Event, July 24–29, 2021 Proceedings
EditorsHelmut Degen, Stavroula Ntoa
Place of PublicationCham Switzerland
Number of pages27
ISBN (Electronic)9783030777722
ISBN (Print)9783030777715
Publication statusPublished - 2021
EventInternational Conference Artificial Intelligence in HCI 2021: Held as Part of the 23rd HCI International Conference HCII 2021 - Online
Duration: 24 Jul 202129 Jul 2021
Conference number: 2nd
https://link.springer.com/book/10.1007/978-3-030-77772-2 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference Artificial Intelligence in HCI 2021
Abbreviated titleAI-HCI 2021
Internet address


  • Bayesian network
  • Context-aware computing
  • Contextual data
  • Human-computer interaction
  • Machine learning
  • Research paper recommender system
  • Scholarly recommender system
  • User experience
  • User modelling

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