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Found 4 papers, 1 papers with code
Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space
In this paper, we propose counterfactual explanation with text-driven concepts (CounTEX), where the concepts are defined only from text by leveraging a pre-trained multi-modal joint embedding space without additional concept-annotated datasets.
Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions.
Learning Personalized Representations using Graph Convolutional Network
Then we propose a graph convolutional network(GCN) based model, namely Personalized Dynamic Routing Feature Encoder(PDRFE), that generates personalized customer representations learned from the built graph.
Convolutional Matrix Factorization for Document Context-Aware Recommendation
However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents.
