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Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed

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Temporal-Aware Multi-behavior Contrastive Recommendationt while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users’ preference evolution, a
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The Role of Familiarity in Implicit Learningex structure. To satisfy the equilibrium, we propose a corresponding hierarchical tree learning algorithm. Furthermore, for those items with a rare appearance in the training data, on which the learning algorithm would fail, we design a dedicated bandit layer to solve them. Extensive experiments on
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The Constructive Nature of Recollectionhe query provided by a user, Query2Trip designs a debiased adversarial learning module by conditional guidance to alleviate this selection bias from positives (visited). The latter happens as unvisited is not equivalent to negative. Query2Trip devises a debiased contrastive learning module by negati
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Michael‘Malisoff,Frédéric Mazenc we first extract high-order collaborative user/item representations with GNNs. Next, we impose a discrepancy regularization term to augment the self-discrimination of the user/item representations. As for the structural view, we initially utilize GNNs to extract high-order features. Next, we utiliz
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