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Found 5 papers, 4 papers with code
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions.
Neural Graph Matching based Collaborative Filtering
User and item attributes are essential side-information; their interactions (i. e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems.
Detecting Beneficial Feature Interactions for Recommender Systems
To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy.
MMF: Attribute Interpretable Collaborative Filtering
Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry.
