Recommender systems are essential to real-world online services due to their substantial business value. Increasingly, large language models (LLMs) have been adopted for recommendation tasks, owing to their strong reasoning capabilities and their effectiveness in handling cold-start items. A large body of work has soug...
Sunwoo Kim, Geon Lee, Kyungho Kim et al.· Proceedings of the 32nd ACM...· 0 citations
NoAH is proposed, a stochastic hypergraph generative model for attributed hypergraphs that utilizes the core–fringe node hierarchy to model hyperedge formation as a series of node attachments and determines attachment probabilities based on node attributes.
Jaewan Chun, Seokbum Yoon, Minyoung Choe et al.· Knowledge and Information Sy...· 0 citations
This work argues that AI agent societies should be studied as a distinct object of inquiry: neither simply collections of individual agents nor merely simulations of human society, and proposes four core properties that a valid AI agent society should satisfy: individualized objectives, rules and governance, autonomy,...
Geon Lee, Fanchen Bu, Soo-Yong Lee et al.· Proceedings of the 32nd ACM...· 1 citation
Large language models (LLMs) have shown promise for training-free recommendation, but LLM-generated user interests are often too broad for fine-grained item retrieval. Existing methods incorporate collaborative filtering (CF) signals in a pre-LLM manner through candidate reranking or prompt augmentation, yielding limit...
Kyungho Kim, Sunwoo Kim, Geon Lee et al.· 0 citations
AI agents are rapidly evolving from isolated personal assistants into networked actors that interact with one another at scale. We envision the emergence of AI agent societies, with their own social and economic dynamics, as a new research frontier. We argue that AI agent societies should be studied as a distinct objec...
Geon Lee, Fanchen Bu, S. Lee et al.· Proceedings of the 32nd ACM...· 1 citation
AEROBAT is introduced, the first multi-agent system to automate behavioral scientific research on AI agents and demonstrates that automated behavioral scientific research on AI agents can complement and extend the reach of manual research.
S. Lee, Jongha Lee, Jaewan Chun et al.· 0 citations
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