1 citations · 1 across the 5 of their papers we have counts for
5 papers
From Time and Place to Preference: LLM-Driven Geo-Temporal Context in Recommendations
Yejin Kim, Shaghayegh Agah, Mayur Nankani +5
Most recommender systems treat timestamps as numeric or cyclical values, overlooking real-world context such as holidays, events, and seasonal patterns. We propose a scalable frame…
Predicting Movie Hits Before They Happen with LLMs
Shaghayegh Agah, Yejin Kim, Neeraj Sharma +4
Addressing the cold-start issue in content recommendation remains a critical ongoing challenge. In this work, we focus on tackling the cold-start problem for movies on a large ente…
Causal Reasoning in Large Language Models: A Knowledge Graph Approach
Yejin Kim, Eojin Kang, Juae Kim +1
Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like c…
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users
Yejin Kim, Scott Rome, Kevin Foley +7
Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage know…
Prompts have evil twins
Rimon Melamed, Lucas H. McCabe, Tanay Wakhare +3
We discover that many natural-language prompts can be replaced by corresponding prompts that are unintelligible to humans but that provably elicit similar behavior in language mode…