31 citations · 68 across the 42 of their papers we have counts for
28 papers · 1 filter
Self-Evolving Memory for Generative Recommendation
Xinyu Lin, Zhuosong Jiang, Zixiao Suo +12
Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation. However, user preferences continuously evolve over time, making self-evolv…
RECAP: Feedback-Driven Streaming Semantic User Profiles for Short-Video Recommendation
Ziyi Zhao, Xiaoyou Zhou, Xiao Lv +13
Language-based user profiles convert long behavioral histories into explicit semantic representations for recommendation. However, most profile generators are optimized in an open…
Uncertainty-aware Generative Recommendation
Chenxiao Fan, Chongming Gao, Yaxin Gong +3
Generative Recommendation has emerged as a transformative paradigm, reformulating recommendation as an end-to-end autoregressive sequence generation task. Despite its promise, exis…
Towards Sample-Efficient and Stable Reinforcement Learning for LLM-based Recommendation
Hongxun Ding, Keqin Bao, Jizhi Zhang +4
While Long Chain-of-Thought (Long CoT) reasoning has shown promise in Large Language Models (LLMs), its adoption for enhancing recommendation quality is growing rapidly. In this wo…
Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation
Yimeng Bai, Chang Liu, Yang Zhang +5
Generative recommendation is emerging as a transformative paradigm by directly generating recommended items, rather than relying on matching. Building such a system typically invol…
Decoding in Latent Spaces for Efficient Inference in LLM-based Recommendation
Chengbing Wang, Yang Zhang, Zhicheng Wang +4
Fine-tuning large language models (LLMs) for recommendation in a generative manner has delivered promising results, but encounters significant inference overhead due to autoregress…
Generative Multi-Target Cross-Domain Recommendation
Jinqiu Jin, Yang Zhang, Fuli Feng +1
Recently, there has been a surge of interest in Multi-Target Cross-Domain Recommendation (MTCDR), which aims to enhance recommendation performance across multiple domains simultane…
Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Shanle Zheng, Keqin Bao, Jizhi Zhang +3
LLM-based recommender systems have made significant progress; however, the deployment cost associated with the large parameter volume of LLMs still hinders their real-world applica…
Brownian Bridge Diffusion for Sequential Recommendation
Yimeng Bai, Yang Zhang, Sihao Ding +5
Diffusion models, known for their strong generative capability derived from iterative noising and denoising processes, have recently emerged as a promising paradigm for sequential…
DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao +3
Generative recommendation is emerging as a powerful paradigm that directly generates item predictions, moving beyond traditional matching-based approaches. However, current methods…
K-order Ranking Preference Optimization for Large Language Models
Shihao Cai, Chongming Gao, Yang Zhang +5
To adapt large language models (LLMs) to ranking tasks, existing list-wise methods, represented by list-wise Direct Preference Optimization (DPO), focus on optimizing partial-order…
Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems
Yimeng Bai, Shunyu Zhang, Yang Zhang +5
Ranking models primarily focus on modeling the relative order of predictions while often neglecting the significance of the accuracy of their absolute values. However, accurate abs…
Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription
Zihao Zhao, Chenxiao Fan, Junlong Liu +5
Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flex…
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…
Agentic Feedback Loop Modeling Improves Recommendation and User Simulation
Shihao Cai, Jizhi Zhang, Keqin Bao +4
Large language model-based agents are increasingly applied in the recommendation field due to their extensive knowledge and strong planning capabilities. While prior research has p…
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…
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…
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…
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…