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Found 5 papers, 1 papers with code
Integrating Large Language Models with Graphical Session-Based Recommendation
SBR has been primarily dominated by Graph Neural Networks, which have achieved many successful outcomes due to their ability to capture both the implicit and explicit relationships between adjacent behaviors.
Natural Language Understanding
Session-Based Recommendations
+2
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems.
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
To address these challenges under open-world assumptions, we design an \textbf{A}daptive \textbf{M}ulti-\textbf{I}nterest \textbf{D}ebiasing framework for cross-domain sequential recommendation (\textbf{AMID}), which consists of a multi-interest information module (\textbf{MIM}) and a doubly robust estimator (\textbf{DRE}).
Neural Node Matching for Multi-Target Cross Domain Recommendation
To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i. e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions.
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.
