437 citations · 1.2k across the 41 of their papers we have counts for
15 papers · 1 filter
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning
Ziang Ye, Zhenru Zhang, Yang Zhang +3
When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we arg…
Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
Chengbing Wang, Yang Zhang, Fengbin Zhu +3
Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation. However, th…
Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning
Keqin Bao, Ming Yan, Yang Zhang +4
Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs…
Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation
Yang Zhang, Juntao You, Yimeng Bai +4
Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, curren…
Personalized Image Generation with Large Multimodal Models
Yiyan Xu, Wenjie Wang, Yang Zhang +4
Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing c…
GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting
Yimeng Bai, Yang Zhang, Fuli Feng +4
Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. H…
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation
Yang Zhang, Keqin Bao, Ming Yan +3
When adapting Large Language Models for Recommendation (LLMRec), it is crucial to integrate collaborative information. Existing methods achieve this by learning collaborative embed…
Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
Xiaoyuan Li, Wenjie Wang, Moxin Li +3
The rapid advancement of Large Language Models (LLMs) in the realm of mathematical reasoning necessitates comprehensive evaluations to gauge progress and inspire future directions.…
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation
Keqin Bao, Jizhi Zhang, Yang Zhang +3
Adapting Large Language Models (LLMs) for recommendation requires careful consideration of the decoding process, given the inherent differences between generating items and natural…
Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
Tianhao Shi, Yang Zhang, Jizhi Zhang +2
As recommender systems are indispensable in various domains such as job searching and e-commerce, providing equitable recommendations to users with different sensitive attributes b…
Large Language Models are Learnable Planners for Long-Term Recommendation
Wentao Shi, Xiangnan He, Yang Zhang +5
Planning for both immediate and long-term benefits becomes increasingly important in recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning capacity b…
Exact and Efficient Unlearning for Large Language Model-based Recommendation
Zhiyu Hu, Yang Zhang, Minghao Xiao +3
The evolving paradigm of Large Language Model-based Recommendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommendat…
The 2nd Workshop on Recommendation with Generative Models
Wenjie Wang, Yang Zhang, Xinyu Lin +7
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This works…
Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Jizhi Zhang, Keqin Bao, Wenjie Wang +5
The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and…
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Wentao Shi, Chenxu Wang, Fuli Feng +4
Optimization metrics are crucial for building recommendation systems at scale. However, an effective and efficient metric for practical use remains elusive. While Top-K ranking met…