21 citations · 33 across the 34 of their papers we have counts for
10 papers · 1 filter
Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training
Haokai Ma, Yunshan Ma, Ruobing Xie +5
Recent research efforts have investigated how to integrate Large Language Models (LLMs) into recommendation, capitalizing on their semantic comprehension and open-world knowledge f…
Multi-Grained Patch Training for Efficient LLM-based Recommendation
Jiayi Liao, Ruobing Xie, Sihang Li +4
Large Language Models (LLMs) have emerged as a new paradigm for recommendation by converting interacted item history into language modeling. However, constrained by the limited con…
RosePO: Aligning LLM-based Recommenders with Human Values
Jiayi Liao, Xiangnan He, Ruobing Xie +5
Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for recommendation systems, which usually adapt a pre-trained LLM to the recommendation scena…
Negative Sampling in Recommendation: A Survey and Future Directions
Haokai Ma, Ruobing Xie, Lei Meng +5
Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of ``in…
Towards Empathetic Conversational Recommender Systems
Xiaoyu Zhang, Ruobing Xie, Yougang Lyu +7
Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language m…
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
Guipeng Xv, Xinyu Li, Ruobing Xie +5
Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the…
DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive e…
ID-centric Pre-training for Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs ar…
Plug-in Diffusion Model for Sequential Recommendation
Haokai Ma, Ruobing Xie, Lei Meng +4
Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommend…
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language
Zekai Qu, Ruobing Xie, Chaojun Xiao +5
With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual in…