428 citations · 705 across the 26 of their papers we have counts for
9 papers · 1 filter
BSL: Understanding and Improving Softmax Loss for Recommendation
Junkang Wu, Jiawei Chen, Jiancan Wu +3
Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendati…
Large Language Model Can Interpret Latent Space of Sequential Recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo +5
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a c…
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Chengpeng Li, Zhengyi Yang, Jizhi Zhang +4
Reinforcement learning (RL) has been widely applied in recommendation systems due to its potential in optimizing the long-term engagement of users. From the perspective of RL, reco…
CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
Yang Zhang, Fuli Feng, Jizhi Zhang +3
Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches…
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
Keqin Bao, Jizhi Zhang, Wenjie Wang +6
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a cru…
Robust Prompt Optimization for Large Language Models Against Distribution Shifts
Moxin Li, Wenjie Wang, Fuli Feng +3
Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. However, their effectiveness is highly dependent on the phrasing of th…