Paper-Conference

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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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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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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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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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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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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