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

• Published in: Journal of intelligent information systems Vol. 57; no. 3; pp. 467 - 489
• Main Authors: Atas, Müslüm, Felfernig, Alexander, Polat-Erdeniz, Seda, Popescu, Andrei, Tran, Thi Ngoc Trang, Uta, Mathias
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Science; Data Structures and Information Theory; Filtration; Information Storage and Retrieval; IT in Business; Natural Language Processing (NLP); Preferences; Psychology; Recommender systems; Preferences; Recommender systems; Cognitive psychology
• ISSN: 0925-9902; 1573-7675
• DOI: 10.1007/s10844-021-00674-5

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.