Tootfinder

Large Language Model-Driven Surrogate-Assisted Evolutionary Algorithm for Expensive Optimization
Lindong Xie, Genghui Li, Zhenkun Wang, Edward Chung, Maoguo Gong
https://arxiv.org/abs/2507.02892

Large Language Model-Driven Surrogate-Assisted Evolutionary Algorithm for Expensive Optimization
Surrogate-assisted evolutionary algorithms (SAEAs) are a key tool for addressing costly optimization tasks, with their efficiency being heavily dependent on the selection of surrogate models and infill sampling criteria. However, designing an effective dynamic selection strategy for SAEAs is labor-intensive and requires substantial domain knowledge. To address this challenge, this paper proposes LLM-SAEA, a novel approach that integrates large language models (LLMs) to configure both surrogate …

Next-User Retrieval: Enhancing Cold-Start Recommendations via Generative Next-User Modeling
Yu-Ting Lan, Yang Huo, Yi Shen, Xiao Yang, Zuotao Liu
https://arxiv.org/abs/2506.15267 …

Next-User Retrieval: Enhancing Cold-Start Recommendations via Generative Next-User Modeling
The item cold-start problem is critical for online recommendation systems, as the success of this phase determines whether high-quality new items can transition to popular ones, receive essential feedback to inspire creators, and thus lead to the long-term retention of creators. However, modern recommendation systems still struggle to address item cold-start challenges due to the heavy reliance on item and historical interactions, which are non-trivial for cold-start items lacking sufficient ex…