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 Knowledge Graph Embedding (KGE)-enhanced recommender systems are
effective in providing accurate and personalized recommendations in diverse application …

Scholarly venue recommendation is an emerging field due to a rapid surge in the number of
scholarly venues concomitant with exponential growth in interdisciplinary research and …

Recommender systems play an indispensable role in today's online businesses. In these
systems, memory-based (neighborhood-based) collaborative filtering is an important …

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

Recommender systems are widely used on the Internet as tools for data analysis,
processing and discovery. Traditional recommendation algorithms mostly exploit rating …

Latent factor-based methods have been extensively employed in recommender systems to
project users and items to the same feature space and use the dot product for predicting …

Deep learning-based collaborative filtering methods are studied in recommendation
systems as efficient feature mapping techniques. The aim of these methods is to project the …

Various recommender systems (RSs) have been developed over recent years, and many of
them have concentrated on English content. Thus, the majority of RSs from the literature …

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 …