3 papers
cs.IR2025
Using LLMs to Capture Users' Temporal Context for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Effective recommender systems demand dynamic user understanding, especially in complex, evolving environments. Traditional user profiling often fails to capture the nuanced, tempor…
cs.IR2025
Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Accurately modeling user preferences is crucial for improving the performance of content-based recommender systems. Existing approaches often rely on simplistic user profiling meth…
cs.IR2025
Towards Explainable Temporal User Profiling with LLMs
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Accurately modeling user preferences is vital not only for improving recommendation performance but also for enhancing transparency in recommender systems. Conventional user profil…