Most recommender systems present recommended products in lists to the user. By doing so, much information is lost about the mutual similarity between recommended products. We propose to represent the mutual similarities of the recommended products in a two dimensional space, where similar...

The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on...

Traditionally, recommender systems present recommendations in lists to the user. In content- and knowledge-based recommendation systems these list are often sorted on some notion of similarity with a query, ideal product specification, or sample product. However, a lot of information is lost in...

In recent years, progresses in data mining and business analytics have fostered the advent of recommender systems, behavioral advertising, and other ways of using consumer data to personalize offers and products. We investigate the incentives for sellers to invest in systems that allow the...

Recommender systems seek to find the interesting items by filtering out the worthless items. Collaborative filtering is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items and...

This study aims to investigate the role of a recommendation system (RS) in personalizing the guest journey in the hospitality industry and its impact on decision making, particularly the exposure effect. The study has two main objectives: (a) to quantify the additional value of using a RS based...

Recommendation-based communication plays a pivotal role in consumer choices. From human sources to electronic word of mouth or different types of recommender systems, recommendations help consumers adopt or reject leads, and can be highly beneficial for organizations. In view of its relevance...