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

Knowledge graph embedding aims to learn distributed representations for entities and
relations, and is proven to be effective in many applications. Crossover interactions--bi …

The widescale use of machine learning algorithms to drive decision-making has highlighted
the critical importance of ensuring the interpretability of such models in order to engender …

Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

Many businesses benefit from the business-to-business (B2B) campaign recommendation
strategy. Complex commodities and user profiles restrict the performance of automated B2B …

Most existing recommender systems leverage user behavior data of one type only, such as
the purchase behavior in E-commerce that is directly related to the business Key …

Customer journey analysis is a hot topic in marketing. Understanding how the customers
behave is crucial and is considered as one of the key drivers of business success. To the …

In this article we propose a framework that generates natural language explanations
supporting the suggestions generated by a recommendation algorithm. The cornerstone of …

Latent factor models (LFMs) such as matrix factorization have achieved the state-of-the-art
performance among various collaborative filtering approaches for recommendation. Despite …