Classification of Normative Recommender Systems

Recommender systems are a primary source for providing user-facing information in a variety of mediums and domains, ranging from movies and news to job advertisements. The potential issues and associated ethical implications have attracted contributions from an interdisciplinary community for studying the normative dimension of recommender systems. However, there has yet to be a shared understanding of the concepts at play and how to operationalize norms and values. We look at normativity from a technical point of view and identify 1.) the pre-processing stage, 2.) the in-processing stage, 3.) the post-processing stage, and 4.) the evaluation stage of a recommender system as the four key areas where normative aspects can be accounted for. Accordingly, four classes of how to implement norms and values in recommender systems are proposed. We proceed with a class-specific comparison of their respective advantages and disadvantages and highlight how such a classification allows us to reason and distinguish between the normative capabilities of recommender systems

AI in Content Curation and Media Pluralism

This part focuses on the use of AI in content curation, addressing the impact of data-driven content recommender systems on diversity and media pluralism. This part and the next one highlighting shortcomings of AI-based content curation and targeted advertising provide human rights-centred recommendations to prevent the negative impact of AI tools in content curation on the right to freedom of opinion and expression

An Empirical Exploration of Perceived Similarity between News Article Texts and Images

The NewsImages task at MediaEval implicitly assumes that there is a one-to-one mapping between news articles and images, given that there is exactly one image that is considered a fit in the evaluation phase. In this quest for insight, we empirically explore this assumption. We conduct a user study where we show participants images from different sources and ask how well the image fits a given article from the NewsImages task. We find that 1.) there can be multiple images per article that are considered equally fitting, 2.) images from within the task dataset can beat the ground truth images for certain articles, and 3.) AI-generated articles underperform in comparison with editorially selected images. Based on our insights, we suggest an alternative evaluation strategy for the task and a clear separation of editorial images and AI-generated conten

Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study

News recommender systems are an increasingly popular field of study that attracts a growing interdisciplinary research community. As these systems play an essential role in our daily lives, the mechanisms behind their curation processes are under scrutiny. In the area of personalized news, many platforms make design choices driven by economic incentives. In contrast to such systems that optimize for financial gain, there can be norm-driven diversity systems that prioritize normative and democratic goals. However, their impact on users in terms of inducing behavioral change or influencing knowledge is still understudied. In this paper, we contribute to the field of news recommender system design by conducting a user study that examines the impact of these normative approaches. We a.) operationalize the notion of a deliberative public sphere for news recommendations, show b.) the impact on news usage, and c.) the influence on political knowledge, attitudes and voting behavior. We find that exposure to small parties is associated with an increase in knowledge about their candidates and that intensive news consumption about a party can change the direction of attitudes of readers towards the issues of the party

Prompt-based Alignment of Headlines and Images Using OpenCLIP

In this paper, we describe how we leverage OpenCLIP to generate automated image recommendations for online news articles for the MediaEval 2023 NewsImages task. By exploring different text prompting techniques, a total of five retrieval approaches were devised. Results show, however, that the best performing approach is an unmodified CLIP version with the raw article headline as input. We reflect on this finding and its implication for future NewsImages tasks

Exploring Graph-querying approaches in LifeGraph

The multi-modal and interrelated nature of lifelog data makes it well suited for graph-based representations. In this paper, we present the second iteration of LifeGraph, a Knowledge Graph for Lifelog Data, initially introduced during the 3rd Lifelog Search Challenge in 2020. This second iteration incorporates several lessons learned from the previous version. While the actual graph has undergone only small changes, the mechanisms by which it is traversed during querying as well as the underlying storage system which performs the traversal have been changed. The means for query formulation have also been slightly extended in capability and made more efficient and intuitive. All these changes have the aim of improving result quality and reducing query time

VideoGraph – Towards Using Knowledge Graphs for Interactive Video Retrieval

Video is a very expressive medium, able to capture a wide variety of information in different ways. While there have been many advances in the recent past, which enable the annotation of semantic concepts as well as individual objects within video, their larger context has so far not extensively been used for the purpose of retrieval. In this paper, we introduce the first iteration of VideoGraph, a knowledge graph-based video retrieval system. VideoGraph combines information extracted from multiple video modalities with external knowledge bases to produce a semantically enriched representation of the content in a video collection, which can then be retrieved using graph traversal. For the 2021 Video Browser Showdown, we show the first proof-of-concept of such a graph-based video retrieval approach. Keywords Interactive video retrieval Knowledge-graphs Multi-modal graph

Diversity in News Recommendation

News diversity in the media has for a long time been a foundational and uncontested basis for ensuring that the communicative needs of individuals and society at large are met. Today, people increasingly rely on online content and recommender systems to consume information challenging the traditional concept of news diversity. In addition, the very concept of diversity, which differs between disciplines, will need to be re-evaluated requiring an interdisciplinary investigation, which requires a new level of mutual cooperation between computer scientists, social scientists, and legal scholars. Based on the outcome of a interdisciplinary workshop, we have the following recommendations, directed at researchers, funders, legislators, regulators, and the media industry: - Conduct interdisciplinary research on news recommenders and diversity. - Create a safe harbor for academic research with industry data. - Strengthen the role of public values in news recommenders. - Create a meaningful governance framework for news recommenders. - Fund a joint lab to spearhead the needed interdisciplinary research, boost practical innovation, develop reference solutions, and transfer insights into practice