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 …

E-business leverages digital channels to scale its functions and services and operates by
connecting and retaining customers using marketing initiatives. To increase the likelihood of …

In this paper, we originally propose an interactive, knowledge-based design recommender
system (IKDRS) for relevant personalised fashion product design schemes with their virtual …

In recent years, new trends and methodologies have emerged that greatly favor the
education sector. E-learning as an alternative to regular teaching and learning processes …

Recommending appropriate drugs to patients based on their history and symptoms is a
complex real-world problem. Knowing whether a drug is useful without its consumption by a …

Product recommendation has played an important role in improving user experiences and
obtaining more profits. To optimize recommendation models, pairwise learning has become …

Recommendation systems have been tremendously important to assist users to find relevant
items. With the information-overloaded problem, it becomes crucial to understand users' …

Recommender Systems focus on implicit and explicit feedback or parameters of users for
better rating prediction. Most of the existing recommender systems use only one type of …

We focus on how to tackle a unique multi-view unsupervised issue: slanderous user
detection, with recurrent neural networks to benefit recommender systems. In real-world …

Collaborative filtering (CF) is one of the dominant techniques used in recommender
systems. Most CF-based methods treat every user (or item) as an isolated existence, without …