A historical perspective of explainable Artificial Intelligence

R Confalonieri, L Coba, B Wagner… - … Reviews: Data Mining …, 2021 - Wiley Online Library
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 …

Explainable recommendation: A survey and new perspectives

Y Zhang, X Chen - Foundations and Trends® in Information …, 2020 - nowpublishers.com
Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

[HTML][HTML] Learning heterogeneous knowledge base embeddings for explainable recommendation

Q Ai, V Azizi, X Chen, Y Zhang - Algorithms, 2018 - mdpi.com
Providing model-generated explanations in recommender systems is important to user
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

Y Himeur, A Alsalemi, A Al-Kababji, F Bensaali… - Information …, 2021 - Elsevier
Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …

Jointly learning explainable rules for recommendation with knowledge graph

W Ma, M Zhang, Y Cao, W Jin, C Wang, Y Liu… - The world wide web …, 2019 - dl.acm.org
Explainability and effectiveness are two key aspects for building recommender systems.
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 …

The future of false information detection on social media: New perspectives and trends

B Guo, Y Ding, L Yao, Y Liang, Z Yu - ACM Computing Surveys (CSUR), 2020 - dl.acm.org
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 …

Transparent, scrutable and explainable user models for personalized recommendation

K Balog, F Radlinski, S Arakelyan - … of the 42nd international acm sigir …, 2019 - dl.acm.org
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 …

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

C Sardianos, I Varlamis, C Chronis… - … Journal of Intelligent …, 2021 - Wiley Online Library
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 …

CAFE: Coarse-to-fine neural symbolic reasoning for explainable recommendation

Y Xian, Z Fu, H Zhao, Y Ge, X Chen, Q Huang… - Proceedings of the 29th …, 2020 - dl.acm.org
Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but more …