41 citations · 163 across the 57 of their papers we have counts for
21 papers · 1 filter
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation
Yucong Luo, Qitao Qin, Hao Zhang +4
Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more…
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Yitong Zhou, Mingyue Cheng, Qingyang Mao +3
Pre-trained foundation models have recently made significant progress in table-related tasks such as table understanding and reasoning. However, recognizing the structure and conte…
PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables
Qingyang Mao, Qi Liu, Zhi Li +3
In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models (LLMs), which have revolutionized nat…
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
Zhiding Liu, Jiqian Yang, Qingyang Mao +5
Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and tra…
Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approach
Qingchuan Li, Jiatong Li, Tongxuan Liu +4
Large Language Models (LLMs) have exhibited remarkable potential across a wide array of reasoning tasks, including logical reasoning. Although massive efforts have been made to emp…
Learning Recommender Systems with Soft Target: A Decoupled Perspective
Hao Zhang, Mingyue Cheng, Qi Liu +3
Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction…
Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
Xiaoyu Tao, Tingyue Pan, Mingyue Cheng +3
Time series classification plays a fundamental role in a wide range of real-world applications. Recently, large language models (LLMs) have demonstrated strong generalization and r…
Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting
Jintao Zhang, Mingyue Cheng, Xiaoyu Tao +2
Time series forecasting models are becoming increasingly prevalent due to their critical role in decision-making across various domains. However, most existing approaches represent…
TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation
Daoyu Wang, Mingyue Cheng, Zhiding Liu +1
Self-supervised learning has garnered increasing attention in time series analysis for benefiting various downstream tasks and reducing reliance on labeled data. Despite its effect…
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
Li Li, Mingyue Cheng, Zhiding Liu +3
Sequential recommendation models user interests based on historical behaviors to provide personalized recommendation. Previous sequential recommendation algorithms primarily employ…
Revisiting the Solution of Meta KDD Cup 2024: CRAG
Jie Ouyang, Yucong Luo, Mingyue Cheng +4
This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA…
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Shuo Yu, Mingyue Cheng, Qi Liu +6
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of…
A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
Mingyue Cheng, Jintao Zhang, Zhiding Liu +1
Intraoperative hypotension (IOH) prediction using past physiological signals is crucial, as IOH may lead to inadequate organ perfusion and significantly elevate the risk of severe…
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential Recommendation
Qitao Qin, Yucong Luo, Mingyue Cheng +2
Federated recommendation (FedRec) preserves user privacy by enabling decentralized training of personalized models, but this architecture is inherently vulnerable to adversarial at…
Advancing Time Series Classification with Multimodal Language Modeling
Mingyue Cheng, Yiheng Chen, Qi Liu +2
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier mod…
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Mingyue Cheng, Hao Zhang, Jiqian Yang +7
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in thi…
Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
Mingyue Cheng, Xiaoyu Tao, Qi Liu +3
Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series representations, which can be very useful in enhancing…
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness
Mingyue Cheng, Hao Zhang, Qi Liu +6
Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{co…
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
Mingyue Cheng, Jiqian Yang, Tingyue Pan +2
Designing effective models for learning time series representations is foundational for time series analysis. Many previous works have explored time series representation modeling…
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Zhiding Liu, Jiqian Yang, Mingyue Cheng +2
Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Neverthel…
Unlocking the Potential of Large Language Models for Explainable Recommendations
Yucong Luo, Mingyue Cheng, Hao Zhang +3
Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in language generation technology, which can enha…