2 citations · 2 across the 12 of their papers we have counts for
12 papers · 1 filter
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…
SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation
Ziqi Xue, Dingxian Wang, Yimeng Bai +7
Generative recommendation has emerged as a scalable alternative to traditional retrieve-and-rank pipelines by operating in a compact token space. However, existing methods mainly r…
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…