Recommender systems are indispensable in e-business due to the extensive product range and the large number of niche articles. Collaborative filtering (CF) algorithms play a central role in generating personalised recommendations. There are numerous CF approaches in the literature that have...

Purpose This research aims to explore the intricate dynamics influencing the adoption of AI-driven smart tourism technologies within the specific context of the United Arab Emirates. It seeks to address the following key questions: How do ethical AI practices and personalization impact tourist...

This paper investigates the impact of social media algorithms on mental health outcomes. I exploit a quasi-experimental setting combining data from the Dutch Longitudinal Internet Studies for the Social Sciences (LISS) coupled with the introduction of the algorithmic feed on Instagram in 2016. I...

We examine the strategic considerations and effects of product placement services in digital content aggregators, focusing in particular on Spotify’s “Discovery Mode”. Discovery Mode introduces bias in users’ consumption bundle that content providers pay through discounted royalties, and...

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products,...

We audit the job recommender algorithms used by four Chinese job boards by creating fictitious applicant profiles that differ only in their gender. Jobs recommended uniquely to the male and female profiles in a pair differ modestly in their observed characteristics, with female jobs advertising...

This paper investigates the value of recommendations for disseminating economic information, with a focus on frictions resulting from preference heterogeneity. We consider Bayesian expected-payoff maximizers who receive non-strategic recommendations by other consumers. The paper provides...

This paper investigates the value of recommendations for disseminating economic information, with a focus on frictions resulting from preference heterogeneity. We consider Bayesian expected-payoff maximizers who receive non-strategic recommendations by other consumers. The paper provides...

This study documents the existence and prevalence of the “review updating” phenomenon, where consumers change the ratings and content of their existing reviews, and examines its implications for platforms and businesses. Using both primary and secondary data, a dataset comprising 3 million...

Selecting the requirements for an information system based on their ranking values is one of the critical steps in software development. Various methods for computing the ranking order of the requirements have been developed using multi-criteria decision-making methods. This study shows...

Recommendation agents (RAs) have proven to be effective decision-making tools for customers, as they can boost trust and loyalty when customers shop online. They can analyse large amounts of data using machine learning algorithms and predictive analytics capabilities to provide highly relevant...

Recently, e-commerce platforms have gained significant attention in the media. As the number of customers using these platforms continues to grow, they face challenges such as limited support, making it increasingly difficult for customers to find answers to their inquiries, leading to a high...

Recommender systems -RS- have experienced exponential growth in various fields, especially in the tourism sector, improving tourism activities' accuracy, personalization, and experience, thus strengthening indicators such as promotion. However, some challenges and opportunities exist to...