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Found 5 papers, 1 papers with code
Text2Bundle: Towards Personalized Query-based Bundle Generation
Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms.
Towards Multi-Subsession Conversational Recommendation
Therefore, we propose a novel conversational recommendation scenario named Multi-Subsession Multi-round Conversational Recommendation (MSMCR), where user would still resort to CRS after several subsessions and might preserve vague interests, and system would proactively ask attributes to activate user interests in the current subsession.
Data-Augmented Counterfactual Learning for Bundle Recommendation
In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation.
Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation
Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation.
Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation
Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences.
