Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

The rapid development of information and communication technologies has enabled a value
co-creation paradigm for develo** industrial products and services, where massive …

ABSTRACT This paper considers Enterprise Information Systems functional architecture and
carries out review of AI applications integrated in Customer Relationship Management …

Background: in recent years, automation has emerged as a hot topic, showcasing its
capacity to perform tasks independently, without constant supervision. While automation has …

Medicine recommendation systems target to recommend a set of medicines given a set of
symptoms which play a crucial role in assisting doctors in their daily clinics. Existing …

Recommendation system is a technology that can mine user's preference for items.
Explainable recommendation is to produce recommendations for target users and give …

Providing personalized explanations for recommendations can help users to understand the
underlying insight of the recommendation results, which is helpful to the effectiveness …

State-of-the-art recommender systems have the ability to generate high-quality
recommendations, but usually cannot provide explanations to humans due to the usage of …

Knowledge graphs (KGs) can help enhance recommendations, especially for the data-
sparsity scenarios with limited user-item interaction data. Due to the strong power of …

Knowledge Graph (KG) is a flexible structure that is able to describe the complex
relationship between data entities. Currently, most KG embedding models are trained based …