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

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The Cine-Tourist’s Map of New Wave Parisrowing applications of Contrastive Learning (CL) with improved user and item representations. However, these contrastive objectives: (1) serve a similar role as the cross-entropy loss while ignoring the item representation space optimisation; and (2) commonly require complicated modelling, including

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Stavros Alifragkis,Giorgos Papakonstantinoumainstream hashtag recommendation faces challenges in the comprehensive difficulty of newly posted tweets in response to new topics, and the accurate identification of mainstream hashtags beyond semantic correctness. However, previous retrieval-based methods based on a fixed predefined mainstream ha

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The Cinematic , as a Site of Postmemoryat once. In this work, we propose a novel .ersatile .lastic .ulti-m.dal (VEMO) model for search-oriented multi-task learning. VEMO is versatile because we integrate cross-modal semantic search, named entity recognition, and scene text spotting into a unified framework, where the latter two can be fu

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https://doi.org/10.1007/978-3-662-63471-4 the optimization algorithm, e.g., grid search or random search, searches for the best hyperparameter configuration according to an optimization-target metric, like . or .. In contrast, the optimized algorithm, e.g., . or ., internally optimizes a different loss function during training, like . or .

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Ceylon Cinnamon Production and Markets,due to limitations in existing training datasets. This study addresses the challenge of generating robust and versatile TOD systems by transforming instructional task descriptions into natural user-system dialogues to serve as enhanced pre-training data. We explore three strategies for synthetic dia