News recommenders are attracting widespread interest in scholarly work. The current research paradigm, however, holds a narrow (mostly user-centered) perspective on the recommendation task. This makes it difficult to understand that their design is in fact the result of a negotiation process among multiple actors involved, such as editors, business executives, technologists and users. To remedy this, a multi-stakeholder recommendation paradigm has been suggested among recommender systems scholars. This work sets out to explore to what extent this paradigm is applicable to the particular context of news recommenders. We conducted 11 interviews with professionals from three leading media companies in Flanders (Belgium) and find that the development of news recommenders is indeed characterized by a negotiation process among multiple stakeholders. However, our results show that the initial multi-stakeholder framework is not adequately accommodating some of our findings, such as the existence of preconditions, the role of product owners, and the indirect involvement of particular stakeholders. Based on our analysis, we suggest an elaborated framework for multi-stakeholder news recommenders that can contribute to scholarship by providing a multi-sided perspective towards the understanding of news recommenders.

Content recommender systems have become commonplace in all digital platforms, and they profoundly alter the media content presented to users. This also applies to news recommender systems (NRSs) used by media companies. However, as it is generally accepted that diverse news coverage is crucial to maintain democratic societies, the role of NRSs is frequently questioned. We assess the development processes of NRSs at three media companies: two private ones active in several European countries, and one public service broadcaster, using a previously established conceptual model for news diversity. Ultimately, we find that news personalization is currently still more of an idea than an actual recurrent practice, and that the application of NRSs is integrated and harmonized across countries and/or other types of new media types (e.g., streaming services) of the same company. We highlight similarities and differences in approaches, objectives and rationales between private and public companies.

Amid the widespread diffusion of digital communication technologies, our cities are at a critical juncture as these technologies are entering all aspects of urban life. Data-driven technologies help citizens to navigate the city, find friends, or discover new places. While these technology-mediated activities come in scope of scholarly research, we lack an understanding of the underlying curation mechanisms that select and present the particular information citizens are exposed to. Nevertheless, such an understanding is crucial to deal with the risk of the socio-cultural polarization assumedly reinforced by this kind of algorithmic curation. Drawing upon the vast amount of work on algorithmic curation in online platforms, we construct an analytical lens that is applied to the urban environment to establish an understanding of algorithmic curation of urban experiences. In this way, this article demonstrates that cities could be considered as a new materiality of curational platforms. Our framework outlines the various urban information flows, curation logics, and stakeholders involved. This work contributes to the current state of the art by bridging the gap between online and offline algorithmic curation and by providing a novel conceptual framework to study this timely topic.

Previous research has used Large Language Models (LLMs) to develop personalized Conversational Recommender Systems (CRS) with text-based user interfaces (UIs). However, the potential of LLMs to generate interactive graphical elements that enhance user experience remains largely unexplored. To address this gap, we introduce "GenUI(ne) CRS," a novel framework designed to leverage LLMs for adaptive and interactive UIs. Our framework supports domain-specific graphical elements such as buttons and cards, in addition to text-based inputs. It also addresses the common LLM issue of outdated knowledge, known as the "knowledge cut-off," by implementing Retrieval-Augmented Generation (RAG). To illustrate its potential, we developed a prototype movie CRS. This work demonstrates the feasibility of LLM-powered interactive UIs and paves the way for future CRS research, including user experience validation, transparent explanations, and addressing LLM biases.