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…