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...

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...

Massive Open Online Courses (MOOCs) and the broad adoption of distance learning over the past few years have caused a remarkable shift in the educational landscape. However, the vast majority of available MOOCs often challenge learners in selecting courses that align with their academic goals,...

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...

Due to rapid technological and Internet advancements, online shopping has become more popular, widespread, convenient, and easier for consumers. Common advantages of online shopping include convenience, product and service variety, price comparison, ability to view online customer reviews,...

The increase in online information and the expanding diversity of user preferences require developing improved recommender systems. Cross-domain recommender systems (CDRS) have emerged as a favorable solution to solve issues related to cold start, data sparsity, and diversity by leveraging...

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...

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...

In the fiercely competitive landscape of the peer-to-peer accommodation platform Airbnb, understanding customers' online recommendation behavior is essential for optimizing competitiveness and harnessing the potential of electronic word-of-mouth. This study aims to provide comprehensive insights...

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...

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...

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...

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,...

In recent years, online shopping is one of the routine parts in people’s life. It is convenient and takes less effort to purchase it. Regarding the increasing revolution of e-commerce businesses, recommendation engine plays a crucial role in them. Recommendation engines are very popular and...

We analyze the employment effects of directing job seekers' applications toward establishments likely to recruit. We run a two-sided randomization design involving about 800,000 job seekers and 40,000 establishments, based on an empirical model that recommends each job seeker to firms so as to...

By introducing the concept of high-reach AI, this paper focuses on AI systems whose widespread use may generate significant risks for both individuals and societies. While some of those risks have been recognised under the AI Act, we analyse the rules laid down by the Digital Services Act (DSA)...