User preferences, intents, behaviors, and contexts evolve over time, making temporal reasoning a fundamental challenge in recommender systems. Accurately capturing both short-term and long-term dynamics is essential for improving personalization and recommendation quality across domains such as e-commerce, media consum...
Adir Solomon, Oren Sar Shalom, T. Kuflik et al.· Proceedings of the 20th ACM...· 0 citations
Point-of-interest (POI) recommender systems typically optimize for accurate next-POI prediction, while personal safety considerations remain implicit or absent. In this demonstration, we present Lumi, a safety-aware POI recommendation system that integrates city-specific contextual features into an LLM-based recommenda...
Maria Alon Kutsaya, Maria Naddaf, Ludovico Boratto et al.· Proceedings of the 20th ACM...· 0 citations
A safety-aware next-POI recommendation method that leverages a Large Language Model (LLM) to generate predictions informed by both mobility patterns and crime-derived safety signals that substantially improves the safety profile of recommended POIs and surpasses state-of-the-art baselines in overall accuracy.
Rami Zaboura, Ludovico Boratto, Adir Solomon· Proceedings of the 20th ACM...· 0 citations
Existing approaches to persona simulation with Large Language Models (LLMs) mostly rely on shallow character descriptions that fail to sustain coherent character behavior across extended interactions. We introduce Deep Persona, a psychologically grounded, three-layered architecture that organizes personas into hierarch...
Rotem Dror, Zohar Elyoseph, Yuval Haber et al.· 0 citations
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