5 citations · 5 across the 6 of their papers we have counts for
6 papers
Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation
Fenglin Yan, Bohao Wang, Jian Zhang +5
Large language models (LLMs) have been widely adopted as backbones for recommender systems. However, their language-centric pretraining makes it difficult to capture collaborative…
IMFuse: Instance-Aware Multi-Layer Fusion for LLM-Enhanced Sequential Recommendation
Yuheng Zheng, Yu Cui, Bin Wu +4
Leveraging large language models (LLMs) to enrich item embeddings with textual semantics has become a prevailing paradigm for enhancing sequential recommendation (SR). However, exi…
From Logs to Language: Learning Optimal Verbalization for LLM-Based Recommendation at Industry Scale
Yucheng Shi, Ying Li, Yu Wang +8
Large language models (LLMs) are promising backbones for generative recommender systems, yet a key challenge remains underexplored: verbalization, i.e., converting structured user…
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…
Sliding Window Training -- Utilizing Historical Recommender Systems Data for Foundation Models
Swanand Joshi, Yesu Feng, Ko-Jen Hsiao +2
Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years. To effectively learn long term user preferences, Large RecSys…
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…