Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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Logic Preference Fusion Reasoning on?Recommendationtract user preferences from interaction records, they frequently neglect the user’s logical requirements, which are embedded in the logical relations between items and entities. Existing methods that account for user’s logical requirements employ neural networks to mimic logical operators, failing t

叢林

Logic Preference Fusion Reasoning on?Recommendationtract user preferences from interaction records, they frequently neglect the user’s logical requirements, which are embedded in the logical relations between items and entities. Existing methods that account for user’s logical requirements employ neural networks to mimic logical operators, failing t

Ointment

MHGNN: Hybrid Graph Neural Network with?Mixers for?Multi-interest Session-Aware Recommendationevements of existing methods, they still have drawbacks in some aspects. Firstly, most existing methods only consider transition relationships between items within the current user’s sessions, while neglecting the valuable item transition patterns from other users and the useful preferences from sim

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MHGNN: Hybrid Graph Neural Network with?Mixers for?Multi-interest Session-Aware Recommendationevements of existing methods, they still have drawbacks in some aspects. Firstly, most existing methods only consider transition relationships between items within the current user’s sessions, while neglecting the valuable item transition patterns from other users and the useful preferences from sim

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Noise-Resistant Graph Neural Networks for?Session-Based Recommendationclick of a user based on a short anonymous interaction sequence. Previous works have focused on users’ long-term and short-term preferences, ignoring the noise problem in session sequences. However, session data is inevitably noisy, as it may contain incorrect clicks that are inconsistent with the u