Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
This survey paper provides a comprehensive analysis of the evolution and current
landscape of recommendation systems, extensively used across various web applications. It …
landscape of recommendation systems, extensively used across various web applications. It …
Reinforcement recommendation reasoning through knowledge graphs for explanation path quality
Abstract Numerous Knowledge Graphs (KGs) are being created to make Recommender
Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the …
Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the …
Reinforced explainable knowledge concept recommendation in MOOCs
In this article, we study knowledge concept recommendation in Massive Open Online
Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units …
Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units …
A survey on modern recommendation system based on big data
A Sun, Y Peng - arxiv e-prints, 2022 - ui.adsabs.harvard.edu
This survey provides an exhaustive exploration of the evolution and current state of
recommendation systems, which have seen widespread integration in various web …
recommendation systems, which have seen widespread integration in various web …
A Review of Explainable Recommender Systems Utilizing Knowledge Graphs and Reinforcement Learning
This review paper addresses the research question of the significance of explainability in AI
and the role of integrating KG and RL to enhance Explainable Recommender Systems …
and the role of integrating KG and RL to enhance Explainable Recommender Systems …
Knowledge is power, understanding is impact: Utility and beyond goals, explanation quality, and fairness in path reasoning recommendation
Path reasoning is a notable recommendation approach that models high-order user-product
relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths …
relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths …
Interpretable Disease Progression Prediction Based on Reinforcement Reasoning Over a Knowledge Graph
Objective: To combine medical knowledge and medical data to interpretably predict the risk
of disease. Methods: We formulated the disease progression prediction task as a random …
of disease. Methods: We formulated the disease progression prediction task as a random …
[PDF][PDF] Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations?
In recent years, we have witnessed an increase in the amount of published research in the
field of Explainable Recommender Systems. These systems are designed to help users find …
field of Explainable Recommender Systems. These systems are designed to help users find …
Knowledge Graph Based on Reinforcement Learning: A Survey and New Perspectives
Q Huo, H Fu, C Song, Q Sun, P Xu, K Qu, H Feng… - IEEE …, 2024 - ieeexplore.ieee.org
Knowledge graph is a form of data representation that uses graph structure to model the
connections between things. The intention of knowledge graph is to optimize the results …
connections between things. The intention of knowledge graph is to optimize the results …
A systematic review of deep knowledge graph-based recommender systems, with focus on explainable embeddings
Recommender systems (RS) have been developed to make personalized suggestions and
enrich users' preferences in various online applications to address the information explosion …
enrich users' preferences in various online applications to address the information explosion …