anagen
发表于 2025-3-26 21:28:01
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鸽子
发表于 2025-3-27 04:00:00
978-3-031-56062-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
Neolithic
发表于 2025-3-27 08:00:54
Rashmi Anoop Patil,Seeram Ramakrishnaion about entities and items. Recently, models based on graph neural networks (GNNs) have adopted knowledge graphs to capture further high-order structural information, such as shared preferences between users and similarities between items. However, existing GNN-based methods suffer from two challe
fibula
发表于 2025-3-27 12:11:53
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Simulate
发表于 2025-3-27 16:54:55
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Favorable
发表于 2025-3-27 18:36:34
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休闲
发表于 2025-3-27 23:35:00
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长处
发表于 2025-3-28 06:09:40
Takuya Nakashima,Tsuyoshi Kawai most well-established retrieval frameworks is the two-stage retrieval pipeline, whereby an inexpensive retrieval algorithm retrieves a subset of candidate documents from a corpus, and a more sophisticated (but costly) model re-ranks these candidates. A notable limitation of this two-stage framework
无效
发表于 2025-3-28 06:24:13
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Cultivate
发表于 2025-3-28 12:55:10
https://doi.org/10.1007/978-3-319-06173-3oisy query consisting of both useful and useless features (e.g., keywords). Our method finds target instances and trains a classifier simultaneously in a greedy strategy: it selects an instance most likely to be of the target class, manually label it, and add it to the training set to retrain the cl