Search Results for author:
Found 2 papers, 1 papers with code
BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations
This approach endeavors to transcend the constraints of the filter bubble, enrich recommendation diversity, and strike a belief balance among users while also catering to user preferences and system-specific business requirements.
Understanding Graph Convolutional Networks for Text Classification
Graph Convolutional Networks (GCN) have been effective at tasks that have rich relational structure and can preserve global structure information of a dataset in graph embeddings.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
