In today’s digital landscape, recommender systems assist users in navigating the vast amount of available data. Within the realm of information access, a subset of such systems called News Recommender Systems help users find news content that interests them. However, by prioritizing traditional accuracy-focused optimization, these systems contribute to the formation of filter bubbles, restricting users’ exposure to diverse viewpoints and exacerbating polarization. To address this issue, beyond-accuracy factors like diversity have been integrated into recommendation. Yet, such approaches can be ineffective and even unintentionally influence user opinions. This raises major ethical concerns as systems lack the legitimacy to shape opinions. This article presents the ADF framework, a novel approach designed to optimize accuracy, diversity, and fairness simultaneously. Unlike conventional models that manage fairness in a tradeoff, ADF establishes fairness as a core constraint. The framework relies on an innovative fairness-constrained diversification strategy, ensuring that users are exposed to a broader range of opinions, without being oriented toward specific viewpoints. ADF is adaptable to various diversity metrics and provides personalized diversification, independent of the underlying recommendation algorithms. Through real-world benchmark datasets and multiple recommendation models, experimental evaluation confirmed that ADF limits the impact on accuracy, enhances diversity, and crucially upholds fairness.

Personalization has changed how we engage with news. While information has become better accessible, users struggle to find information in the vast amount of news and news commentary published on a daily basis. The INRA workshop provides a forum to researchers, practitioners, and interested parties to discuss recent trends concerning news personalization. This edition of INRA highlights a variety of topics including generative AI, fake news, and multi-modality. Generative AI facilitates creating content at a rapid pace. That includes misleading information that can further erode the trust in media organizations. Texts and still images have dominated the era of printed news. Now, news organizations publish their information also in the form of podcasts and videos.

Fake news, hate speech and offensive language are related evil triplets currently affecting modern societies. Text modality for the computational detection of these phenomena has been widely used. In recent times, multimodal studies in this direction are attracting a lot of interests because of the potentials offered by other modalities in contributing to the detection of these menaces. However, a major problem in multimodal content understanding is how to effectively model the complementarity of the different modalities due to their diverse characteristics and features. From a multimodal point of view, the three tasks have been studied mainly using image and text modalities. Improving the effectiveness of the diverse multimodal approaches is still an open research topic. In addition to texts and images, we consider image-texts which are rarely used in previous studies but which contain useful information for enhancing the effectiveness of a prediction model. In order to ease multimodal content understanding and enhance prediction, we leverage recent advances in computer vision and deep learning for these tasks. First, we unify the modalities by creating a text representation of the images and image-texts, in addition to the main text. Secondly, we propose a multi-layer deep neural network with inter-modal attention mechanism to model the complementarity among these modalities. We conduct extensive experiments involving three standard datasets covering the three tasks. Experimental results show that detection of fake news, hate speech and offensive language can benefit from this approach. Furthermore, we conduct robust ablation experiments to show the effectiveness of our approach. In overall experiments, our model predominantly outperforms prior works across the datasets.

Artificial Intelligence is transforming the news eco-system at a rapid pace. Large Language Models have emerged and facilitate producing content in larger quantities and with less skill or technical oversight. At the same time, media organizations struggle to maintain public trust as misinformation and disinformation continue to spread. The 11th International Workshop on News Recommendation and Analytics (INRA) serves as a venue for exchanging ideas, discussing recent developments, and important issues concerning news. We welcome contributions as scientific articles, demonstrations, and innovative ideas or citicism. Our goal is to bring together both academia and practitioners to address vital challenges facing the media world. The workshop gives attendees the chance to learn about ongoing research, discuss technical as well as ethical aspects of personalization, and contemplate about how technology, in particular Artificial Intelligence, will affect the way humans engage with news. Topics of interest include Large Language Models, advances in news personalization, mis- and disinformation, and user experience.

A rapidly changing news ecosystem presents new challenges to research, media organizations, consumers, and societies. The 10th edition of the International Workshop on News Recommendation and Analytics (INRA) serves to exchange ideas and discuss recent trends, technological advancements, and open problems concerning news. We welcome contributions in scientific articles, demonstrations, and ideas. We strive to bring together researchers, practitioners, and decision-makers to address crucial challenges. The workshop provides an opportunity to learn about recent research and interactively discuss technical and interdisciplinary aspects related to news. Topics of interest include information access systems for news, advances in natural language processing, multi-modality, mis- and disinformation, trust and user experiences, and personalization.

News portals, social media, and news recommender systems have a strong influence on the perception of events. The way with which people engage with news has changed. Today, we encounter personalized access to news. On the one hand, personalization allows us to manage the overwhelming amount of information. On the other hand, personalization can create a set of problems such as filter bubbles, privacy and disinformation related problems. News analytics helps us to develop solutions towards the challenges created by personalization. News analytics helps us to understand the news ecosystem better and develop solutions towards the challenges of news recommender systems. The 9th International Workshop on News Recommendation and Analytics (INRA 2021) provides a forum to discuss recent trends and observations related to news recommendation, personalization, and analytics. The interdisciplinary workshop connects research from machine learning and analytics, algorithmic modelling and prediction, as well as results from ethical and psychological research.

Publishing news represents a vital function for societal health. News recommender systems, which support readers finding relevant content, face challenges beyond those encountered by other types of recommender systems. They have to deal with a dynamic flow of unstructured, fragmentary, and potentially unreliable news stories. The International Workshop on News Recommendation and Analytics (INRA) focuses on the challenges of news recommender systems and aims to connect researchers, practitioners and journalists. The seventh edition of INRA takes place as a half-day workshop in conjunction with thirteenth ACM Conference on Recommender Systems (RecSys '19) on September 16--20, 2019 in Copenhagen, Denmark. INRA 2019 focuses on the news recommender systems under three main categories: News recommendation, news analytics, and ethical aspects of news recommendation.

Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time- and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.

With the increasing ubiquity of access to online news sources, the news recommender systems are becoming widely popular in recent days. However, providing interesting news for each user is a challenging task in highly-dynamic news domain. Many news aggregator sites such as Google News suggest its users to provide sign in to the system for getting user-specific (relevant) news articles. For more generic news recommendation, the system collects user click history and page access pattern implicitly. Often the users are not sure about the usage of the collected and consolidated data by the recommender systems which they usually trade for receiving the news recommendation. Privacy of user identity, user behavior in terms of page access patterns contributes to the overall privacy risks in the news domain. This review paper discusses the current state-of-the-art of privacy risks and existing privacy preserving approaches in the news domain from user perspective.