Papers with Session-based recommendation

2 papers
Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation (2025.coling-main)

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Challenge: Existing methods rely on user actions within the current session, overlooking the wealth of auxiliary information available.
Approach: They propose a session-based recommendation model that leverages the current session graph and similar session graphs to capture the intrinsic relationships between items.
Outcome: The proposed model improves on the Diginetica dataset by 2.00% and 10.70% respectively.
Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)

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Challenge: Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios.
Approach: They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity.
Outcome: The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods.

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