30 citations · 85 across the 70 of their papers we have counts for
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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…
Beyond Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising
Zipeng Chen, Jiaer Zheng, Xiangyang Xu +15
Recent advances in LLM-based user simulation have shown promise for offline evaluation of recommendation and advertising systems. However, existing simulators typically infer user…
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7
The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive system…
Verifiable Reasoning for LLM-based Generative Recommendation
Xinyu Lin, Hanqing Zeng, Hanchao Yu +8
Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing…
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…
Bringing Reasoning to Generative Recommendation Through the Lens of Cascaded Ranking
Xinyu Lin, Pengyuan Liu, Wenjie Wang +5
Generative Recommendation (GR) has become a promising end-to-end approach with high FLOPS utilization for resource-efficient recommendation. Despite the effectiveness, we show that…
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…
Heterogeneous User Modeling for LLM-based Recommendation
Honghui Bao, Wenjie Wang, Xinyu Lin +4
Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation. A key cha…
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…
EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens
Chaoqun Yang, Xinyu Lin, Wenjie Wang +4
Large Language Model-based generative recommendation (LLMRec) has achieved notable success, but it suffers from high inference latency due to massive computational overhead and mem…
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…
NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
Sunhao Dai, Wenjie Wang, Liang Pang +4
Generative AI search is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple we…
Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics
Chen Xu, Jujia Zhao, Wenjie Wang +4
Fairness is an increasingly important factor in re-ranking tasks. Prior work has identified a trade-off between ranking accuracy and item fairness. However, the underlying mechanis…
SemCORE: A Semantic-Enhanced Generative Cross-Modal Retrieval Framework with MLLMs
Haoxuan Li, Yi Bin, Yunshan Ma +4
Cross-modal retrieval (CMR) is a fundamental task in multimedia research, focused on retrieving semantically relevant targets across different modalities. While traditional CMR met…
Exploring Training and Inference Scaling Laws in Generative Retrieval
Hongru Cai, Yongqi Li, Ruifeng Yuan +4
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel par…
Personalized Generation In Large Model Era: A Survey
Yiyan Xu, Jinghao Zhang, Alireza Salemi +6
In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents t…
Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation
Chen Xu, Yuxin Li, Wenjie Wang +3
Group max-min fairness (MMF) is commonly used in fairness-aware recommender systems (RS) as an optimization objective, as it aims to protect marginalized item groups and ensures a…
Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking
Ruiyang Ren, Yuhao Wang, Junyi Li +4
In the era of vast digital information, the sheer volume and heterogeneity of available information present significant challenges for intricate information seeking. Users frequent…
Order-agnostic Identifier for Large Language Model-based Generative Recommendation
Xinyu Lin, Haihan Shi, Wenjie Wang +4
Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning…
Self-Calibrated Listwise Reranking with Large Language Models
Ruiyang Ren, Yuhao Wang, Kun Zhou +5
Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passa…
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…
Efficient Inference for Large Language Model-based Generative Recommendation
Xinyu Lin, Chaoqun Yang, Wenjie Wang +5
Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caus…
Learnable Item Tokenization for Generative Recommendation
Wenjie Wang, Honghui Bao, Xinyu Lin +5
Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data in…
A Taxation Perspective for Fair Re-ranking
Chen Xu, Xiaopeng Ye, Wenjie Wang +3
Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure responsibility and ethics. The exploration of redistribution problems has a long history in…