Dynamic Modeling of User Preferences for Stable Recommendations

Olaleke, Oluwafemi; Oseledets, Ivan; Frolov, Evgeny

doi:10.1145/3450613.3456830

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Shared control between the vehicle and the driver should be guaranteed during this interval. One of the biggest challenges is to create a system that conveys the message in a clear, explicit way, while at the same time allowing for the possibility of continued automated control of the vehicle in the event that the driver cannot take over [11].…”

Section: Driver Response Timementioning

confidence: 99%

Driver Model for Take-Over-Request in Autonomous Vehicles

Barisic,

Sigrist,

Oliver

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

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This work presents a driver model for Take-Over-Request (TOR) in autonomous vehicles (AVs) that considers the driver's mental state and response time during the transition from automatic to manual driving modes. The Fallback Ready User (FRU) is introduced as a key component that defines the minimum attention required from the driver to respond to TORs and system failures. Highly adaptive FRU models are essential for ensuring the safety of AVs. By using nonintrusive methods, such as facial expression tracking, to capture the driver's mental state and improve AV safety we study shared control between the vehicle and driver during TOR. The paper presents two application scenarios for TOR analysis in ADAVEC system: detecting when the driver is ready for TOR and reacting to unpredictable situations, such as driver sickness or drowsiness. The proposed driver model considers user personalization based on high-level features, long-term changes, and real-time evidence. CCS CONCEPTS• Human-centered computing → User models; User centered design; • Software and its engineering → Empirical software validation; • Computing methodologies → Modeling methodologies; • Applied computing → Transportation.

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Section: Driver Response Timementioning

confidence: 99%

Driver Model for Take-Over-Request in Autonomous Vehicles

Barisic,

Sigrist,

Oliver

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

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

et al. 2021

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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.

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