A historical perspective of explainable Artificial Intelligence
Abstract Explainability in Artificial Intelligence (AI) has been revived as a topic of active
research by the need of conveying safety and trust to users in the “how” and “why” of …
research by the need of conveying safety and trust to users in the “how” and “why” of …
Explainable recommendation: A survey and new perspectives
Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …
recommendations but also intuitive explanations. The explanations may either be post-hoc …
[HTML][HTML] Learning heterogeneous knowledge base embeddings for explainable recommendation
Providing model-generated explanations in recommender systems is important to user
experience. State-of-the-art recommendation algorithms—especially the collaborative …
experience. State-of-the-art recommendation algorithms—especially the collaborative …
[HTML][HTML] A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …
Jointly learning explainable rules for recommendation with knowledge graph
Explainability and effectiveness are two key aspects for building recommender systems.
Prior efforts mostly focus on incorporating side information to achieve better …
Prior efforts mostly focus on incorporating side information to achieve better …
[HTML][HTML] A comprehensive survey of knowledge graph-based recommender systems: Technologies, development, and contributions
J Chicaiza, P Valdiviezo-Diaz - Information, 2021 - mdpi.com
In recent years, the use of recommender systems has become popular on the web. To
improve recommendation performance, usage, and scalability, the research has evolved by …
improve recommendation performance, usage, and scalability, the research has evolved by …
The future of false information detection on social media: New perspectives and trends
The massive spread of false information on social media has become a global risk, implicitly
influencing public opinion and threatening social/political development. False information …
influencing public opinion and threatening social/political development. False information …
Transparent, scrutable and explainable user models for personalized recommendation
Most recommender systems base their recommendations on implicit or explicit item-level
feedback provided by users. These item ratings are combined into a complex user model …
feedback provided by users. These item ratings are combined into a complex user model …
The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency
The recent advances in artificial intelligence namely in machine learning and deep learning,
have boosted the performance of intelligent systems in several ways. This gave rise to …
have boosted the performance of intelligent systems in several ways. This gave rise to …
CAFE: Coarse-to-fine neural symbolic reasoning for explainable recommendation
Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but more …
recommender systems, not only to achieve better recommendation performance, but more …