蚊子
发表于 2025-3-26 23:07:46
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Inferior
发表于 2025-3-27 03:15:50
Multi-level and Multi-interest User Interest Modeling for News Recommendations the minimum interest modeling unit when modeling user’s interests. They ignored the low-level and high-level signals from user’s behaviors. In this paper, we propose a news recommendation method combined with multi-level and multi-interest user interest modeling, named MMRN. In contrast to existin
GRAIN
发表于 2025-3-27 07:09:17
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micronized
发表于 2025-3-27 11:26:16
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Lipohypertrophy
发表于 2025-3-27 15:45:45
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Projection
发表于 2025-3-27 21:38:30
A 2D Entity Pair Tagging Scheme for Relation Triplet Extractiondes, extensive experiments on two public datasets widely used by many researchers are conducted, and the experimental results perform better than the state-of-the-art baselines overall and deliver consistent performance gains on complex scenarios of various overlapping patterns and multiple triplets
Preamble
发表于 2025-3-28 00:28:21
MAGNN-GC: Multi-head Attentive Graph Neural Networks with Global Context for Session-Based Recommendssion items with the learned global-level and local-level item embeddings using the multi-head attention mechanism. Additionally, we use the focal loss as a loss function to adjust sample weights and address the problem of imbalanced positive and negative samples during model training. Our experimen
饮料
发表于 2025-3-28 03:07:31
Chinese Relation Extraction with Bi-directional Context-Based Lattice LSTMention semantic interaction-enhanced (CSI) classifier promotes exchange of semantic information between hidden states from forward and backward perspectives for more comprehensive representations of relation types. In experiments conducted on two public datasets from distinct domains, our method yie
溃烂
发表于 2025-3-28 09:39:21
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命令变成大炮
发表于 2025-3-28 12:24:23
Debiased Contrastive Loss for Collaborative Filteringof our methods in automatically mining the hard negative instances. Experimental results on three public benchmarks demonstrate that the proposed debiased contrastive loss can augment several existing MF and GNN-based CF models and outperform popular learning objectives in the recommendation. Additi