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Abstract Personalized news experiences powered by recommender systems permeate our lives and have the potential to influence not only our opinions, but also our decisions. At the same time, the content and viewpoints contained within news recommendations are driven by multiple factors, including both personalization and editorial selection. Explanations could help users gain a better understanding of the factors contributing to the news items selected for them to read. Indeed, recent works show that explanations are essential for users of news recommenders to understand their consumption preferences and set intentions in line with their goals, such as goals for knowledge development and increased diversity of content or viewpoints. We give examples of such works on explanation and interactive interface interventions which have been effective in influencing readers' consumption intentions and behaviors in news recommendations. However, the state‐of‐the‐art in news recommender systems currently fall short in terms of evaluating such interventions in live systems, limiting our ability to measure their true impact on user behavior and opinions. To help understand the true benefit of these interfaces, we therefore call for improving the realism of studies for news. 

Recruiting older adults for research studies is a challenging endeavor. We conducted an interview to understand older adults’ preferences and expectations, with the goal of building a recommender system to support the selection of suitable research studies. Our findings suggest that sharing the results of the studies they participated in would motivate older adults to participate in more studies and give them a feeling of self-accomplishment and belonging. We list 15 design implications based on our user research and present a prototype system based on these design implications. 

Many retired people engage in volunteer opportunities as a means to give back to their communities, stay physically and intellectually active, and build and expand their social networks. However, our semi-structured interviews of six retirees found that they typically avoid searching for volunteer opportunities through websites and social media due to a lack of trust in those tools and a concern for privacy. Instead, they rely on word-of-mouth communication facilitated through emails with individuals and organizations they trust. To support this type of communication, we designed an adaptive interaction mechanism in the form of a newsletter with volunteer opportunities that are personalized using recommender system technology. The newsletter mechanism leverages personal connections through user-defined preference-based communities that allow users to share volunteer opportunities with their peers. 

While the algorithms used by music streaming services to provide recommendations have often been studied in offline, isolated settings, little research has been conducted studying the nature of their recommendations within the full context of the system itself. This work seeks to compare the level of diversity of the real-world recommendations provided by five of the most popular music streaming services, given the same lists of low-, medium- and high-diversity input items. We contextualized our results by examining the reviews for each of the five services on the Google Play Store, focusing on users’ perception of their recommender systems and the diversity of their output. We found that YouTube Music offered the most diverse recommendations, but the perception of the recommenders was similar across the five services. Consumers had multiple perspectives on the recommendations provided by their music service—ranging from not wanting any recommendations to applauding the algorithm for helping them find new music.