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.

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.

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.

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys.

With the ubiquity of fast internet connections and the growing availability of Video-On-Demand (VOD) services powerful recommender systems are needed. Traditionally, movie recommender systems apply user-based collaborative filtering providing high quality recommendations if users maintain user profiles describing preferences and movie ratings. The shortcomings of Collaborative Filtering are that comprehensive user profiles are required and users tend to get recommendations very similar to the user profile "filter bubble". In addition, CF-based recommenders neither consider current trends nor the context. In order to overcome these weaknesses, we develop a system identifying interesting events in the stream of current news and deploying this information for computing recommendations. Our system gathers topics of interest from Twitter and RSS-Feeds, extracts relevant Named Entities, and uses semantic relations for recommending movies closely related to these topics. We explain the used algorithms and show that our system provides highly relevant recommendations.

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.

We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular, we review a signed variant of the graph Laplacian. We derive our results by considering random walks, graph clustering, graph drawing and electrical networks, showing that they all result in the same formalism for handling negatively weighted edges. We illustrate our methods using examples from social networks with negative edges and bipartite rating graphs.

We analyse the corpus of user relationships of the Slashdot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative endorsements. We adapt social network analysis techniques to the problem of negative edge weights. In particular, we consider signed variants of global network characteristics such as the clustering coefficient, node-level characteristics such as centrality and popularity measures, and link-level characteristics such as distances and similarity measures. We evaluate these measures on the task of identifying unpopular users, as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to be used. We compare our methods to traditional methods which are only suitable for positively weighted edges.