Towards psychology-aware preference construction in recommender systems: Overview and research issues

Müslüm Atas, Alexander Felfernig*, Seda Polat-Erdeniz, Andrei Popescu, Thi Ngoc Trang Tran, Mathias Uta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for example, as keywords, categories, and item ratings. In conversational recommendation approaches such as constraint-based and critiquing-based recommendation, user preferences are often represented on the semantic level in terms of item attribute values and critiques. In this article, we provide an overview of preference representations used in different types of recommender systems. In this context, we take into account the fact that preferences aren’t stable but are rather constructed within the scope of a recommendation process. In which way preferences are determined and adapted is influenced by various factors such as personality traits, emotional states, and cognitive biases. We summarize preference construction related research and also discuss aspects of counteracting cognitive biases.
Original languageEnglish
Pages (from-to)467-489
Number of pages23
JournalJournal of Intelligent Information Systems
Issue number3
Early online date2021
Publication statusPublished - Dec 2021


  • Cognitive psychology
  • Preferences
  • Recommender systems

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

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