The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes
guidelines on how information systems (IS) research could contribute to this research …

Large Language Models (LLMs) have demonstrated remarkable performance across
diverse domains, thereby prompting researchers to explore their potential for use in …

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …

Post-hoc explanations of machine learning models are crucial for people to understand and
act on algorithmic predictions. An intriguing class of explanations is through counterfactuals …

A filter bubble refers to the phenomenon where Internet customization effectively isolates
individuals from diverse opinions or materials, resulting in their exposure to only a select set …

Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

This paper proposes a recommender system to alleviate the cold-start problem that can
estimate user preferences based on only a small number of items. To identify a user's …

Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …