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.

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.

An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user’s gender or age should not influence the model. Personalized recommender systems are particularly prone to violating this definition through their explicit user focus and user modelling. Explicit user modelling is also an aspect that makes many recommender systems incapable of providing hitherto unseen users with recommendations. We propose novel approaches for mitigating discrimination in Variational Autoencoder-based recommender systems by limiting the encoding of demographic information. The approaches are capable of, and evaluated on, providing users that are not represented in the training data with fair recommendations.

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.

News organizations employ personalized recommenders to target news articles to specific readers and thus foster engagement. Existing approaches rely on extensive user profiles. However frequently possible, readers rarely authenticate themselves on news publishers' websites. This paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined short-term and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

Recommender System research has evolved to focus on developing algorithms capable of high performance in online systems. This development calls for a new evaluation infrastructure that supports multi-dimensional evaluation of recommender systems. Today's researchers should analyze algorithms with respect to a variety of aspects including predictive performance and scalability. Researchers need to subject algorithms to realistic conditions in online A/B tests. We introduce two resources supporting such evaluation methodologies: the new data set of stream recommendation interactions released for CLEF NewsREEL 2017, and the new Open Recommendation Platform (ORP). The data set allows researchers to study a stream recommendation problem closely by "replaying" it locally, and ORP makes it possible to take this evaluation "live" in a living lab scenario. Specifically, ORP allows researchers to deploy their algorithms in a live stream to carry out A/B tests. To our knowledge, NewsREEL is the first online news recommender system resource to be put at the disposal of the research community. In order to encourage others to develop comparable resources for a wide range of domains, we present a list of practical lessons learned in the development of the dataset and ORP.

In our fast changing world, data streams move into the focus. In this paper, we study recommender systems for news portals. Compared with traditional recommender scenarios based on static data sets, the short life cycle of news items and the dynamics in users' preferences are major challenges when developing news recommender systems. This motivates us to research methods facilitating the inclusion of context and trends into news recommender systems. We explain specific requirements for news recommender system and discuss approaches incorporating trends and temporal user habits in order to improve news recommender system. A detailed data analysis motivates our approach. In addition, we discuss experiences of applying news recommendation algorithms online. The evaluation shows that approaches come with specific strengths and weaknesses. Consequently, publishers should select the recommendation strategy with the specific requirements in mind.

Recommending news articles entails additional requirements to recommender systems. Such requirements include special consumption patterns, fluctuating itemcollections, and highly sparse user profiles. This workshop (NRS'[email protected]) brought together researchers and practitioners around the topics of designing and evaluating novel news recommender systems. Additionally, we offered a challenge allowing participants to evaluate their recommendation algorithms with actual user feedback.

The Recommender System Challenge 2012 invited participants to work on two tracks with real-world datasets and to submit their contributions that would be related to specific problem contexts. First of all, it asked participants to develop new algorithms and to compare them to other algorithms in given settings; in addition, it asked participants to explore with new recommendation methods, services, as well as added-value services related to recommendation.