Human decision making and recommender systems

Li CHEN, Marco De Gemmis, Alexander Felfernig, Pasquale Lops, Francesco Ricci, Giovanni Semeraro

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

Abstract

Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.

Original languageEnglish
Article numberA17
JournalACM Transactions on Interactive Intelligent Systems
Volume3
Issue number3
DOIs
Publication statusPublished - Oct 2013

Scopus Subject Areas

  • Human-Computer Interaction
  • Artificial Intelligence

User-Defined Keywords

  • Algorithms
  • Design
  • Experimentation
  • Human Factors

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