Publications

Recognizing unseen objects via multimodal intensive knowledge graph propagation
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new …
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Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating …
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Exploring large language model for graph data understanding in online job recommendations
Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in …
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Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model
Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series …
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Advancing Time Series Classification with Multimodal Language Modeling
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common …
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ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
This paper introduces ConvTimeNet, a novel deep hierarchical fully convolutional network designed to serve as a general-purpose model …
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Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and …
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Supporting Your Idea Reasonably: A Knowledge-Aware Topic Reasoning Strategy for Citation Recommendation
With the explosive growth of scholarly information, researchers spend much time and effort copiously quoting authoritative works to …
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AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes …
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Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
Deep learning models have progressively advanced time series forecasting due to their powerful capacity in capturing sequence …
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A general tail item representation enhancement framework for sequential recommendation
Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems (SRS). …
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Unlocking the Potential of Large Language Models for Explainable Recommendations
Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in …
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Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture …
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Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering
Researchers have successfully adapted the privacy-preserving Federated Learning (FL) to news recommendation tasks to better protect …
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Communication-efficient federated learning with stagewise training strategy
The efficiency of communication across workers is a significant factor that affects the performance of federated learning. Though …
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KMF: knowledge-aware multi-faceted representation learning for zero-shot node classification
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict …
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Using Entropy for Group Sampling in Pairwise Ranking from implicit feedback
In recent years, pairwise methods, such as Bayesian Personalized Ranking (BPR), have gained significant attention in the field of …
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Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense
Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes …
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A survey on large language models for recommendation
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained …
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Learning the explainable semantic relations via unified graph topic-disentangled neural networks
Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCNs) can effectively learn node representations via aggregating …
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FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification …
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TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become …
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