23 citations · 27 across the 7 of their papers we have counts for
9 papers
Routing Between Generative and Collaborative User Profiles: A Serving-Time Gate for Controllable Novelty
Milad Sabouri, Neeraj Sharma, Sardar Hamidian +1
Large language models (LLMs) enable rich semantic user profiles for recommendation, but such profiles are more expensive to generate and are not necessarily desirable to deploy uni…
When LLM-Inferred User Context Adds Value in Production Streaming Recommendation
Milad Sabouri, Neeraj Sharma, Sardar Hamidian +1
Contextual information in recommender systems is shifting from static, predefined variables toward latent representations inferred from behavior. Large language models support this…
When LLM-Based User Profiling Adds Value in Production Streaming Recommendation
Milad Sabouri, Neeraj Sharma, Sardar Hamidian +1
Personalized recommendation depends critically on how user representations are constructed from historical behavior. Two paradigms have emerged for constructing semantic user profi…
Effectiveness of LLMs in Temporal User Profiling for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Effectively modeling the dynamic nature of user preferences is crucial for enhancing recommendation accuracy and fostering transparency in recommender systems. Traditional user pro…
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…
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…
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…
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Kun Lin, Masoud Mansoury, Farzad Eskandanian +2
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations…
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher +3
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when…