Titlebook: Advances in Information Retrieval; 46th European Confer Nazli Goharian,Nicola Tonellotto,Iadh Ounis Conference proceedings 2024 The Editor(

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978-3-031-56062-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

Neolithic

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

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Simulate

Favorable

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长处

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

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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