Recommender systems are efficient tools for filtering online information, which is
widespread owing to the changing habits of computer users, personalization trends, and …

Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

The flipped classroom approach is aimed at improving learning outcomes by promoting
learning motivation and engagement. Recommendation systems can also be used to …

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~(LLM) like ChatGPT have become indispensable to artificial
general intelligence~(AGI), demonstrating excellent performance in various natural …

Extensive research has been conducted on e-tourism spanning a wide range of concepts,
challenges and concerns discussed in tourism recommender systems (TRS). Smart tourism …

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

Graph neural network (GNN) is effective in modeling high-order interactions and has been
widely used in various personalized applications such as recommendation. However …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

As new applications of artificial intelligence continue to emerge, there is an increasing
interest to explore how this type of technology can improve automated service interactions …