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 …

Abstract Information technology has spread widely, and extraordinarily large amounts of
data have been made accessible to users, which has made it challenging to select data that …

A commercially viable multi-stakeholder recommendation system maximizes the utility gain
by learning the personalized preferences of multiple stakeholders, such as consumers and …

Matrix Factorization (MF) is one of the most popular techniques used in Collaborative
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …

Visual media, in today's world, has swept across most forms of day to day communication. In
the paradigm of generative modelling, restricted Boltzmann machines (RBMs) are used to …

Most existing recommendation methods focus on the improvement of recommender
accuracy while ignoring the influence of recommended explanation. Recommender …

A content-based recommender system uses essential item features that play a crucial role in
building quality user preference profiles. However, in most real-world datasets, the item …

One of the most commonly used techniques in the recommendation framework is
collaborative filtering (CF). It performs better with sufficient records of user rating but is not …

In designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

The recommendation of suitable products/items for a group of users has always been a
difficult task. Most of the recommender systems are designed for individual use only …