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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KRec-C2: A Knowledge Graph Enhanced Recommendation with?Context Awareness and?Contrastive Learningpplications: high-quality knowledge graphs and modeling user-item relationships. However, existing methods try to solve the above challenges by adopting unified relational rules and simple node aggregation, which cannot cope with complex structured graph data. In this paper, we propose a .nowledge g

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HIT: Learning a?Hierarchical Tree-Based Model with?Variable-Length Layers for?Recommendation Systemsng structure is a practical solution to retrieve and recommend the most relevant items within a limited response time. The existing approaches that adopted embedding or tree-based index structures cannot handle the long-tail phenomenon. To address this issue, we propose a .erarchical .ree-based mode

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Thompson Sampling with?Time-Varying Reward for?Contextual Banditsorithms utilize a fixed reward mechanism, which makes it difficult to accurately capture the preference changes of users in non-stationary environments, thus affecting recommendation performance. In this paper, we formalize the online recommendation task as a contextual bandit problem and propose a

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Query2Trip: Dual-Debiased Learning for?Neural Trip Recommendation-specific query. Recent neural TripRec methods with sequence-to-sequence models have achieved remarkable performance. However, alongside the exposure bias in general recommender systems, the selection bias caused by the lack of explicit feedback (e.g., ratings) from the trip data exacerbates the ten

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A New Reconstruction Attack: User Latent Vector Leakage in?Federated Recommendationre kept on its local device and thus are private to others. However, keeping the training data locally can not ensure the user’s privacy is compromised. In this paper, we show that the existing FR is vulnerable to a new reconstruction attack in which the attacker leverages the semi-trusted FR server

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Dual-View Self-supervised Co-training for?Knowledge Graph Recommendationly improve model performance, has attracted considerable interest. Currently, KGR community has focused on designing Graph Neural Networks (GNNs)-based end-to-end KGR models. Unfortunately, existing GNNs-based KGR models are focused on extracting high-order attributes (knowledge) but suffer from res

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