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- CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
- StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning
- A Comprehensive Survey of the LLM-Based Agent: The Contextual Cognition Perspective
- Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
- TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
- Position: Beyond Model-Centric Prediction — Agentic Time Series Forecasting
- InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
- Hierarchical multimodal llms with semantic space alignment for enhanced time series classification
- Global Structure-aware and Feature-augmented Graph Neural Network for Heterophilic Graphs
- Improving Time Series Forecasting via Instance-aware Post-hoc Revision
- Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
- TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via LLMs
- Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce
- Tag-augmented Dual-target Cross-domain Recommendation
- Preference Trajectory Modeling via Flow Matching for Sequential Recommendation
- Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
- Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
- A Comprehensive Survey of Time Series Forecasting: Concepts, Challenges, and Future Directions
- A Survey on Table Mining with Large Language Models: Challenges, Advancements and Prospects
- A Survey on Knowledge-Oriented Retrieval-Augmented Generation
- InstrucTime: Advancing Time Series Classification with Multimodal Language Modeling
- Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information
- Revisiting the Solution of Meta KDD Cup 2024: CRAG
- Recognizing unseen objects via multimodal intensive knowledge graph propagation
- Empowering Sequential Recommender Systems from Mixture of Collaborative Signals and Semantic Relatedness
- Learning Recommender Systems with Soft Target: A Decoupled Perspective
- Unlocking the Potential of Large Language Models for Explainable Recommendations
- Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
- Exploring large language model for graph data understanding in online job recommendations
- Supporting Your Idea Reasonably: A Knowledge-Aware Topic Reasoning Strategy for Citation Recommendation
- Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
- A General Tail Item Representation Enhancement Framework for Sequential Recommender Systems
- Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering
- Communication-efficient federated learning with stagewise training strategy
- KMF: knowledge-aware multi-faceted representation learning for zero-shot node classification
- Using Entropy for Group Sampling in Pairwise Ranking from implicit feedback
- A survey on large language models for recommendation
- Learning the explainable semantic relations via unified graph topic-disentangled neural networks
- FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
- One Person, One Model - Learning Compound Router for Sequential Recommendation
- Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
- Nested Named Entity Recognition from Medical Texts: A Multi-task Learning Approach
- Collaborative List-and-Pairwise Filtering from Implicit Feedback
- Towards Automatic Designing of Deep Hybrid Network Architecture for Sequential Recommendation
- Learning Transferrable User Representations with Sequential Behaviors via Contrastive Pre-training
- LawyerPAN: A Proficiency Assessment Network for Trial Lawyers
- Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement
- Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering