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Found 8 papers, 1 papers with code
Towards Automatic Bias Detection in Knowledge Graphs
With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident.
Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks
Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users.
Adversarial Learning for Debiasing Knowledge Graph Embeddings
As a second step, we explore gender bias in KGE, and a careful examination of popular KGE algorithms suggest that sensitive attribute like the gender of a person can be predicted from the embedding.
A Deep Learning Pipeline for Patient Diagnosis Prediction Using Electronic Health Records
Second, machine learning algorithms that predict multiple disease diagnosis categories simultaneously remain underdeveloped.
Cross-Cutting Political Awareness through Diverse News Recommendations
The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias.
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems.
