Research ZeilingerOpen OpportunitiesThe development of Large Language Models (LLMs), like ChatGPT and GPT-4, has influenced
the field of Natural Language Processing and Artificial Intelligence with their exceptional proficiency in comprehending and generating language, alongside their notable generalization and reasoning abilities. Consequently, recent research efforts have focused on leveraging the capabilities of LLMs to improve recommender systems. Recommender systems significantly influence human behavior by shaping users’ preferences, decision-making processes, and overall engagement with digital content. This project develops on the interpretation of recommender systems (controller) in feedback interaction with the users (system), [3]. By following a similar approach to [2], we will investigate how a careful integration of a LLM with a Model Predictive Control (MPC) framework can enhance recommender systems by ensuring accurate and adaptable recommendations while considering user preferences and constraints.
Understanding the influence of recommender systems over users behaviour and managing it effectively will be enhanced through the MPC framework, which offers a structured and interpretable approach to recommendation optimization. - Automotive Engineering, Computer Communications Networks, Electrical Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
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