1 citations · 1 across the 3 of their papers we have counts for
4 papers
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction
Sejoon Oh, Fernando Amat Gil, Evan Smothers +11
Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to recommend, where each SID is a s…
Netflix Artwork Personalization via LLM Post-training
Hyunji Nam, Sejoon Oh, Emma Kong +2
Large language models (LLMs) have demonstrated success in various applications of user recommendation and personalization across e-commerce and entertainment. On many entertainment…
IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning
Sejoon Oh, Moumita Bhattacharya, Yesu Feng +1
Recommender systems have played a critical role in diverse digital services such as e-commerce, streaming media, social networks, etc. If we know what a user's intent is in a given…
Hierarchical Multi-Task Learning Framework for Session-based Recommendations
Sejoon Oh, Walid Shalaby, Amir Afsharinejad +1
While session-based recommender systems (SBRSs) have shown superior recommendation performance, multi-task learning (MTL) has been adopted by SBRSs to enhance their prediction accu…