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 …

Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …

Abstract Content-based filtering (CBF), one of the most successful recommendation
techniques, is based on correlations between contents. CBF uses item information …

Clustering is a fundamental data exploration technique to discover hidden grou**
structure of data. With the proliferation of big data, and the increase of volume and variety …

Recommender Systems are currently highly relevant for hel** users deal with the
information overload they suffer from the large volume of data on the web, and automatically …

Adaptive learning systems aim to provide learning items tailored to the behavior and needs
of individual learners. However, one of the outstanding challenges in adaptive item selection …

Co-clustering aims to explore coherent patterns by simultaneously clustering samples and
features of data. Several co-clustering methods have been proposed in the past decades …

Recommender systems have been widely used in various domains including movies, news,
music with an aim to provide the most relevant proposals to users from a variety of available …

The emergence and penetration of smart mobile devices has given rise to the development
of context-aware systems that utilize sensors to collect available data about users in order to …

Over the past decade, recommendation systems have been one of the most sought after by
various researchers. Basket analysis of online systems' customers and recommending …