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 …

Recently, recommender systems have played an increasingly important role in a wide
variety of commercial applications to help users find favourite products. Research in the …

Large-scale undirected weighted networks are frequently encountered in big-data-related
applications concerning interactions among a large unique set of entities. Such a network …

Matrix Factorization is a successful approach for generating an effective recommender
system. However, most existing matrix factorization methods suffer from the sparsity and cold …

Collaborative filtering (CF) is a widely applied method to perform recommendation tasks in a
wide range of domains and applications. Dictionary learning (DL) models, which are highly …

Recommender systems in social networks are widely used for connecting users to their
desired items from a vast catalog of available items. Learning the user's preferences from all …

Traditional recommendation techniques are hindered by the simplicity and sparsity of user-
item interaction data and can be improved by introducing auxiliary information related to …

Urban road travel time estimation and prediction on a citywide scale is a necessary and
important task for recommending optimal travel paths. However, this problem has not yet …

Collaborative filtering is a technique that takes into account the common characteristics of
users and items in recommender systems. Matrix decompositions are one of the most used …

Recommender systems (RSs) are effective technologies and tools used to deal with the
problems of information overload, and have been developed rapidly in nearly two decades …