most citedCollaborative Feature Learning for Fine-grained Facial Forgery Detection and Segmentation

2 citations · 3 across the 12 of their papers we have counts for

collaborators
Showing cs.IRShow all

2 papers · 1 filter

cs.IR2023

Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation

Bo Peng, Ben Burns, Ziqi Chen +2

In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhance…

cs.IR2023

Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

Bo Peng, Srinivasan Parthasarathy, Xia Ning

Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-ba…