6 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…
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…
Architecture is All You Need: Improving LLM Recommenders by Dropping the Text
Kevin Foley, Shaghayegh Agah, Kavya Priyanka Kakinada
In recent years, there has been an explosion of interest in the applications of large pre-trained language models (PLMs) to recommender systems, with many studies showing strong pe…
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…