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Found 10 papers, 0 papers with code
Entity Prediction in Knowledge Graphs with Joint Embeddings
Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly.
QAGCN: A Graph Convolutional Network-based Multi-Relation Question Answering System
Reasoning-based methods with complex reasoning mechanisms, such as reinforcement learning-based sequential decision making, have been regarded as the default pathway for this task.
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.
Implementations in Machine Ethics: A Survey
Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology.
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.
A Comparative Survey of Recent Natural Language Interfaces for Databases
Each of the systems is evaluated using a curated list of ten sample questions to show their strengths and weaknesses.
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.
FedMark: A Marketplace for Federated Data on the Web
The problem is that it is not clear how publishers of commercial data can monetize their data in this new setting.
Databases
