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Found 7 papers, 4 papers with code
Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
In this paper, we propose Turbo-CF, a GF-based CF method that is both training-free and matrix decomposition-free.
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature.
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays.
Node Feature Augmentation Vitaminizes Network Alignment
Network alignment (NA) is the task of discovering node correspondences across multiple networks.
Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation
Network alignment (NA) is the task of discovering node correspondences across different networks.
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data
We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line.
On the Power of Gradual Network Alignment Using Dual-Perception Similarities
Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes.
