Andere Kunden interessierten sich auch für
![Human-Computer Integration]( "Human-Computer Integration")
Human-Computer Integration60,99 €![Collaborative Filtering Recommender Systems]( "Collaborative Filtering Recommender Systems")
Collaborative Filtering Recommender Systems82,99 €![Towards Better User Studies in Computer Graphics and Vision]( "Towards Better User Studies in Computer Graphics and Vision")
Towards Better User Studies in Computer Graphics and Vision55,99 €![Statistical Methods for Recommender Systems]( "Statistical Methods for Recommender Systems")
Statistical Methods for Recommender Systems57,99 €![Active Learning for Recommender Systems]( "Active Learning for Recommender Systems")
Active Learning for Recommender Systems18,99 €![Applying Recommender Systems to Defend National Borders from Smuggling]( "Applying Recommender Systems to Defend National Borders from Smuggling")
Applying Recommender Systems to Defend National Borders from Smuggling12,99 €![A personalized Social Network Based Recommender system: MOVBOK]( "A personalized Social Network Based Recommender system: MOVBOK")
A personalized Social Network Based Recommender system: MOVBOK24,99 €
Personalized recommender systems have become indispensable in today's online world. Most of today's recommendation algorithms are data-driven and based on behavioral data. While such systems can produce useful recommendations, they are often uninterpretable, black-box models that do not incorporate the underlying cognitive reasons for user behavior in the algorithms' design. This survey presents a thorough review of the state of the art of recommender systems that leverage psychological constructs and theories to model and predict user behavior and improve the recommendation process - so-called psychology-informed recommender systems. The survey identifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affectaware recommender systems. For each category, the authors highlight domains in which psychological theory plays a key role. Further, they discuss selected decision-psychological phenomena that impact the interaction between a user and a recommender. They also focus on related work that investigates the evaluation of recommender systems from the user perspective and highlight user-centric evaluation frameworks, and potential research tasks for future work at the end of this survey.