From the 1 of 5 linked papers with an AI index.
5 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
The paper introduces IMFuse, a method that adaptively combines representations from multiple layers of large language models to improve sequential recommendation, using instance-aw…
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…
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…