Session-based Recommender based on Normalized Graph Neural Network with Margin Penalty
Author: 
Qinxin GaoAuthors Info & Claims
Pages 65 - 71
Abstract
With the gradual development of e-commerce, Session-Based Recommendation (SBR) for anonymous users have gradually received more and more attention from researchers. Existing graph-based SBR systems lack the ability to distinguish sessions with the same last item and are sensitive to the long-tail distribution of items. To address this problem, a novel Session-based Recommender based on Normalized Graph neural network with Margin Penalty (SR-NGMP) is proposed. In SR-NGMP, a mandatory margin is introduced to ensure that sessions with the same last item difference between them, and L2 penalty is applied to alleviate the long-tailed distribution of items. The SR-NGMP is then experimented on two real-world datasets, and proved our SR-NGMP achieves a superior performance over state-of-the-art methods
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Index Terms
- Session-based Recommender based on Normalized Graph Neural Network with Margin Penalty
Recommendations
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningPredicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect user historical sessions while modeling user preference, ...
Attentive Capsule Graph Neural Networks for Session-Based Recommendation
Knowledge Science, Engineering and ManagementAbstractConsidering recommendation scenarios in that user profiles are anonymous, the session-based recommendation is proposed to predict the items users are interested in from short sessions. However, most existing methods for session-based ...
Session-based recommendation with time-aware neural attention network
Highlights- Propose a session-based recommendation model based on graph attention mechanism.
AbstractIn situations where user information and detailed knowledge of user behaviors are challenging to obtain, the session-based recommendation is essential. The session-based recommendation (SBR) relies on the current anonymous session ...
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Published In
November 2023
406 pages
ISBN:9798400708268
DOI:10.1145/3603273
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Published: 09 January 2024
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AAIA 2023
AAIA 2023: 2023 International Conference on Advances in Artificial Intelligence and Applications
November 18 - 20, 2023
Wuhan, China
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