SOURCE-LINKED INTELLIGENCE

Intelligent Embodiment: Social Robots that Understand and Adapt Through Recommender Systems

CORDIS · observation · Publication date unknown

lities in personalization and adaptivity, which are crucial for ensuring long-term user engagement. Meanwhile, given the rapid growth of the AI-driven e-commerce market and the widespread adoption of Large Language Models, conversational recommender systems have become a popular tool for providing recommendations and information. While most recommenders effectively sustain long-term user engagement in real-world applications, they are typically regarded as non-embodied agents, overlooking their social roles in interactions. SOCIALADAPT aims to enable social robots and recommender systems to benefit from each other and achieves the following objectives: for social robots, i) inferring user preferences from multi-modal interactions, and ii) personalized and adaptive responses for long-term use; for recommender systems, iii) integrating conversational recommenders with humanoid embodiments, and iv) evaluating the embodied recommender. To address the first objective, the candidate will use multimodal recommendation methods to infer short-term and long-term user preferences. For the seco

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR