65 citations · 66 across the 7 of their papers we have counts for
10 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…
Content Moderation in TV Search: Balancing Policy Compliance, Relevance, and User Experience
Adeep Hande, Kishorekumar Sundararajan, Sardar Hamidian +1
Millions of people rely on search functionality to find and explore content on entertainment platforms. Modern search systems use a combination of candidate generation and ranking…
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…
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users
Yejin Kim, Scott Rome, Kevin Foley +7
Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage know…
A Quantitative and Qualitative Analysis of Schizophrenia Language
Amal Alqahtani, Efsun Sarioglu Kay, Sardar Hamidian +2
Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it…
Automatically Identifying Political Ads on Facebook: Towards Understanding of Manipulation via User Targeting
Or Levi, Sardar Hamidian, Pedram Hosseini
The reports of Russian interference in the 2016 United States elections brought into the center of public attention concerns related to the ability of foreign actors to increase so…
Rumor Detection and Classification for Twitter Data
Sardar Hamidian, Mona T Diab
With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spre…