activity
20222026
most citedWhen Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities

338 citations · 454 across the 70 of their papers we have counts for

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13 papers · 1 filter

cs.AI2026

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration

Hang Lv, Hongchao Gu, Ruiqing Yang +5

Generative listwise reranking leverages global context for superior retrieval but is plagued by intrinsic position bias, where models exhibit structural sensitivity to input order…

cs.AI2026

SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility

Xuyang Zhi, Peilun zhou, Chengqiang Lu +10

The evolution of Large Language Models (LLMs) is shifting the focus from single, verifiable tasks toward complex, open-ended real-world scenarios, imposing significant challenges o…

cs.AI2026

Generative Data Transformation: From Mixed to Unified Data

Jiaqing Zhang, Mingjia Yin, Hao Wang +6

Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start,…

cs.AI2026

Model Specific Task Similarity for Vision Language Model Selection via Layer Conductance

Wei Yang, Hong Xie, Tao Tan +3

While open sourced Vision-Language Models (VLMs) have proliferated, selecting the optimal pretrained model for a specific downstream task remains challenging. Exhaustive evaluation…

cs.AI2025

HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking

Runquan Gui, Zhihai Wang, Jie Wang +7

Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathe…

cs.AI2024

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Tingjia Shen, Hao Wang, Chuhan Wu +7

Scaling Laws have emerged as a powerful framework for understanding how model performance evolves as they increase in size, providing valuable insights for optimizing computational…

cs.AI2024★ 2 cited

UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models

Liu Qi, He Yongyi, Lian Defu +4

Multimodal Entity Linking (MEL) is a crucial task that aims at linking ambiguous mentions within multimodal contexts to the referent entities in a multimodal knowledge base, such a…

cs.AI2024

Analytical and Empirical Study of Herding Effects in Recommendation Systems

Hong Xie, Mingze Zhong, Defu Lian +2

Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, t…

cs.AI2024

Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities

Hong Xie, Jinyu Mo, Defu Lian +2

Motivated by distributed selection problems, we formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures stochastic arrival of requests to each arm, a…

cs.AI2024★ 2 cited

WESE: Weak Exploration to Strong Exploitation for LLM Agents

Xu Huang, Weiwen Liu, Xiaolong Chen +5

Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning…

cs.AI2024★ 34 cited

Understanding the planning of LLM agents: A survey

Xu Huang, Weiwen Liu, Xiaolong Chen +6

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This surve…

cs.AI2024

Securing Recommender System via Cooperative Training

Qingyang Wang, Chenwang Wu, Defu Lian +1

Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. Among existing defense methods, data-processing-based methods inevitably…

cs.AI2023★ 3 cited

Large-Scale OD Matrix Estimation with A Deep Learning Method

Zheli Xiong, Defu Lian, Enhong Chen +2

The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current…