32 citations · 74 across the 18 of their papers we have counts for
19 papers
Token-Controlled Re-ranking for Sequential Recommendation via LLMs
Wenxi Dai, Wujiang Xu, Pinhuan Wang +1
The widespread adoption of Large Language Models (LLMs) as re-rankers is shifting recommender systems towards a user-centric paradigm. However, a significant gap remains: current r…
Graph4MM: Weaving Multimodal Learning with Structural Information
Xuying Ning, Dongqi Fu, Tianxin Wei +2
Real-world multimodal data usually exhibit complex structural relationships beyond traditional one-to-one mappings like image-caption pairs. Entities across modalities interact in…
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Xi Zhu, Haochen Xue, Ziwei Zhao +7
Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically exhibit distinctive structure and domain…
OmniRouter: Budget and Performance Controllable Multi-LLM Routing
Kai Mei, Wujiang Xu, Minghao Guo +2
Large language models (LLMs) deliver superior performance but require substantial computational resources and operate with relatively low efficiency, while smaller models can effic…
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search
Zujie Liang, Feng Wei, Wujiang Xu +3
Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low…
iAgent: LLM Agent as a Shield between User and Recommender Systems
Wujiang Xu, Yunxiao Shi, Zujie Liang +6
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, th…
A-MEM: Agentic Memory for LLM Agents
Wujiang Xu, Zujie Liang, Kai Mei +3
While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current mem…
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding
Mingyu Jin, Kai Mei, Wujiang Xu +5
Large language models (LLMs) have achieved remarkable success in contextual knowledge understanding. In this paper, we show that these concentrated massive values consistently emer…
Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction
Yunxiao Shi, Wujiang Xu, Haimin Zhang +2
Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhausti…
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Can Jin, Hongwu Peng, Shiyu Zhao +7
Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR) across various modules, such as reranking. Despite impressive performance, current zero-shot rel…
MoralBench: Moral Evaluation of LLMs
Jianchao Ji, Yutong Chen, Mingyu Jin +3
In the rapidly evolving field of artificial intelligence, large language models (LLMs) have emerged as powerful tools for a myriad of applications, from natural language processing…
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
Mingming Ha, Xuewen Tao, Wenfang Lin +3
In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are gene…
iVAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation
Xuying Ning, Wujiang Xu, Tianxin Wei +1
Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across multiple domains to mitigate data sparsity and cold-start challenges in Single-Domain Sequential Recom…
SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
Wujiang Xu, Qitian Wu, Zujie Liang +5
Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user…
AIOS: LLM Agent Operating System
Kai Mei, Xi Zhu, Wujiang Xu +7
LLM-based intelligent agents face significant deployment challenges, particularly related to resource management. Allowing unrestricted access to LLM or tool resources can lead to…
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Wujiang Xu, Xuying Ning, Wenfang Lin +7
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. The existing approaches a…
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang +5
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CD…
Neural Node Matching for Multi-Target Cross Domain Recommendation
Wujiang Xu, Shaoshuai Li, Mingming Ha +5
Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) s…
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
Wujiang Xu, Runzhong Wang, Xiaobo Guo +6
Video summarization intends to produce a concise video summary by effectively capturing and combining the most informative parts of the whole content. Existing approaches for video…