Titlebook: Web Engineering; 24th International C Kostas Stefanidis,Kari Syst?,Elisa Quintarelli Conference proceedings 2024 The Editor(s) (if applicab

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書目名稱Web Engineering影響因子(影響力)




書目名稱Web Engineering影響因子(影響力)學(xué)科排名




書目名稱Web Engineering網(wǎng)絡(luò)公開(kāi)度




書目名稱Web Engineering網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Web Engineering被引頻次




書目名稱Web Engineering被引頻次學(xué)科排名




書目名稱Web Engineering年度引用




書目名稱Web Engineering年度引用學(xué)科排名




書目名稱Web Engineering讀者反饋




書目名稱Web Engineering讀者反饋學(xué)科排名

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Inclusive Counterfactual Generation: Leveraging LLMs in?Identifying Online Hate chief component within the existing counterfactual data augmentation pipeline, however, involves manually flipping labels and making minimal content edits to training data. In a hate speech context, these forms of editing have been shown to still retain offensive hate speech content. Inspired by th

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Decentralized Search over?Personal Online Datastores: Architecture and?Performance Evaluation where individuals can control access to their data. Solid allows developers to deploy applications with access to data stored in pods, subject to users’ permission. For the decentralised Web to succeed, the problem of search over pods with varying access permissions must be solved. The ESPRESSO fra

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AutoMaster: Differentiable Graph Neural Network Architecture Search for Collaborative Filtering Recoms (RSs). In recent years, there has been a heated debate on whether the non-linear propagation mechanism in Graph Convolutional Networks (GCNs) is suitable for CF tasks, and the performance of linear propagation is believed to be superior to non-linear propagation mainly in the field of RSs. Theref

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A Multi-model Recurrent Knowledge Graph Embedding for?Contextual Recommendationsy to incorporate this kind of assorted data. This paper introduces a context-aware recommender, based on deriving graph embeddings by learning the representations of appropriate meta-paths mined from a graph database. Our system uses several LSTMs to model the meta-path semantics between a user-item