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Found 7 papers, 2 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.
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
In this paper, we propose a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously.
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content.
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors.
