6 citations · 12 across the 32 of their papers we have counts for
33 papers
Empowering Compact LLMs with Fusion of Layer-wise Exits for Recommendation
Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen +3
Large language model-based recommender systems (LLM-RSs) have demonstrated remarkable capabilities, but are computationally unsustainable for many real-world applications. Compact…
ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning
Jiaqi Zhang, Tong Chen, Junliang Yu +2
Agentic systems have rapidly advanced in their ability to interact with real-world environments, leverage external tools, and provide services for users. However, unlike natural-wo…
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
Shutong Qiao, Wei Yuan, Tong Chen +3
Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization. However, these artificial codes…
FINER-SQL: Boosting Small Language Models for Text-to-SQL
Thanh Dat Hoang, Thanh Trung Huynh, Matthias Weidlich +4
Large language models have driven major advances in Text-to-SQL generation. However, they suffer from high computational cost, long latency, and data privacy concerns, which make t…
ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems
Yi Zhang, Yiwen Zhang, Kai Zheng +2
The remarkable text understanding and generation capabilities of large language models (LLMs) have revitalized the field of general recommendation based on implicit user feedback.…
Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
Zongwei Wang, Min Gao, Hongzhi Yin +5
Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling itera…
Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation
Lei Guo, Hongyun Yang, Pengjie Ren +3
Large Language Models have shown great success in recommender systems. However, the limited and sparse nature of user data often restricts the LLM's ability to effectively model be…
AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views
Minh Tam Pham, Trinh Pham, Tong Chen +3
Text-to-SQL is the task of translating natural language queries into executable SQL for a given database, enabling non-expert users to access structured data without writing SQL ma…
Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time Scaling
Wei Jiang, Tong Chen, Wei Yuan +2
Vision-language models (VLMs) have been proven effective for detecting multi-modal misinformation on social platforms, especially in zero-shot settings with unavailable or delayed…
Integrating Vision-Centric Text Understanding for Conversational Recommender Systems
Wei Yuan, Shutong Qiao, Tong Chen +3
Conversational Recommender Systems (CRSs) have attracted growing attention for their ability to deliver personalized recommendations through natural language interactions. To more…
Efficient Content-based Recommendation Model Training via Noise-aware Coreset Selection
Hung Vinh Tran, Tong Chen, Hechuan Wen +3
Content-based recommendation systems (CRSs) utilize content features to predict user-item interactions, serving as essential tools for helping users navigate information-rich web s…
QA-Merging: Query-Adaptive Reasoning via Layer Selective Model Merging
Zhaofeng Zhong, Wei Yuan, Tong Chen +4
Recent large reasoning models (LRMs) have achieved strong performance on complex reasoning tasks by generating a long chain-of-thought (Long-CoT). However, such lengthy reasoning i…
A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback
Thanh Dat Hoang, Thanh Trung Huynh, Matthias Weidlich +4
Text2SQL, the task of generating SQL queries from natural language text, is a critical challenge in data engineering. Recently, Large Language Models (LLMs) have demonstrated super…
TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding
Xiaobo Xing, Wei Yuan, Tong Chen +3
Modeling semantic and structural information from tabular data remains a core challenge for effective table understanding. Existing Table-as-Text approaches flatten tables for larg…
Cloud-Device Collaborative Agents for Sequential Recommendation
Jing Long, Sirui Huang, Huan Huo +3
Recent advances in large language models (LLMs) have enabled agent-based recommendation systems with strong semantic understanding and flexible reasoning capabilities. While LLM-ba…
Towards Propagation-aware Representation Learning for Supervised Social Media Graph Analytics
Wei Jiang, Tong Chen, Wei Yuan +3
Social media platforms generate vast, complex graph-structured data, facilitating diverse tasks such as rumor detection, bot identification, and influence modeling. Real-world appl…
Controllable and Stealthy Shilling Attacks via Dispersive Latent Diffusion
Shutong Qiao, Wei Yuan, Junliang Yu +3
Recommender systems (RSs) are now fundamental to various online platforms, but their dependence on user-contributed data leaves them vulnerable to shilling attacks that can manipul…
Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model
Zhaofeng Zhong, Wei Yuan, Liang Qu +4
With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face t…
Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts
Yunke Qu, Liang Qu, Tong Chen +2
Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing u…
Watermarking Large Language Model-based Time Series Forecasting
Wei Yuan, Chaoqun Yang, Yu Xing +3
Large Language Model-based Time Series Forecasting (LLMTS) has shown remarkable promise in handling complex and diverse temporal data, representing a significant step toward founda…
Lightweight Embeddings with Graph Rewiring for Collaborative Filtering
Xurong Liang, Tong Chen, Wei Yuan +1
As recommendation services scale rapidly and their deployment now commonly involves resource-constrained edge devices, GNN-based recommender systems face significant challenges, in…
Multi-agents based User Values Mining for Recommendation
Lijian Chen, Wei Yuan, Tong Chen +3
Recommender systems have rapidly evolved and become integral to many online services. However, existing systems sometimes produce unstable and unsatisfactory recommendations that f…
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation
Yuchuan Zhao, Tong Chen, Junliang Yu +3
Sequential recommender systems (SRSs) excel in capturing users' dynamic interests, thus playing a key role in various industrial applications. The popularity of SRSs has also drive…
Sparser Training for On-Device Recommendation Systems
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems often rely on large embedding tables that map users and items to dense vectors of uniform size, leading to substantial memory consumption and inefficiencies. Th…