1 citations · 1 across the 29 of their papers we have counts for
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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…
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
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…
MiniRec: Data-Efficient Reinforcement Learning for LLM-based Recommendation
Lin Wang, Yang Zhang, Jingfan Chen +4
The integration of reinforcement learning (RL) into large language models (LLMs) has opened new opportunities for recommender systems by eliciting reasoning and improving user pref…
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…
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…
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…
NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Xiaoyan Zhao, Juntao You, Yang Zhang +5
Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users…
Inference Computation Scaling for Feature Augmentation in Recommendation Systems
Weihao Liu, Zhaocheng Du, Haiyuan Zhao +5
Large language models have become a powerful method for feature augmentation in recommendation systems. However, existing approaches relying on quick inference often suffer from in…