10 citations · 18 across the 14 of their papers we have counts for
14 papers
PrefReward: Learning User Preference Matrix for Personalized Text Generation
Yue Wu, Chengbing Wang, Yimeng Bai +3
Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing person…
Intuition-Guided Latent Reasoning for LLM-Based Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, motivating their use for preference reasoning in recommender syst…
Distribution-Level Contrastive Supervision for Generative Recommendation
Ziqi Xue, Dingxian Wang, Yimeng Bai +7
Recent generative recommenders improve scalability by retrieving items through token generation instead of traditional ranking over large candidate sets. Yet their training signals…
UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimization
Jialei Li, Yang Zhang, Yimeng Bai +7
Generative recommendation has recently emerged as a transformative paradigm that directly generates target items, surpassing traditional cascaded approaches. It typically involves…
MERGE: Next-Generation Item Indexing Paradigm for Large-Scale Streaming Recommendation
Jing Yan, Yimeng Bai, Zongyu Liu +7
Item indexing, which maps a large corpus of items into compact discrete representations, is critical for both discriminative and generative recommender systems, yet existing Vector…
Unveiling Inference Scaling for Difference-Aware User Modeling in LLM Personalization
Suyu Chen, Yimeng Bai, Yulong Huang +2
Large Language Models (LLMs) are increasingly integrated into users' daily lives, driving a growing demand for personalized outputs. Prior work has primarily leveraged a user's own…
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…
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…
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…
Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization
Yilun Qiu, Xiaoyan Zhao, Yang Zhang +5
Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personaliza…
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…
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…
LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
Yimeng Bai, Yang Zhang, Jing Lu +5
Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has eme…