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Found 5 papers, 1 papers with code
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.
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations
The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks.
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes.
