NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS by Richard H. Thaler and Cass R. Sunstein Penguin Books, 2009, 312 pp, ISBN 978-0-14-311526-7This book is best described formally as a general explanation of and advocacy for libertarian paternalism, a term coined by the authors in earlier publications. Informally, it is about how leaders, systems, organizations, and governments can nudge people to do the things the nudgers want and need done for the betterment of the nudgees, or of society. It is paternalism in the sense that "it is legitimate for choice architects to try to influence people's behavior in order to make their lives longer, healthier, and better", (p. 5) It is libertarian in that "people should be free to do what they like - and to opt out of undesirable arrangements if they want to do so", (p. 5) The built-in possibility of opting out or making a different choice preserves freedom of choice even though people's behavior has been influenced by the nature of the presentation of the information or by the structure of the decisionmaking system. I had never heard of libertarian paternalism before reading this book, and I now find it fascinating.Written for a general audience, this book contains mostly social and behavioral science theory and models, but there is considerable discussion of structure and process that has roots in mathematical and quantitative modeling. One of the main applications of this social system is economic choice in investing, selecting and purchasing products and services, systems of taxes, banking (mortgages, borrowing, savings), and retirement systems. Other quantitative social choice systems discussed include environmental effects, health care plans, gambling, and organ donations. Softer issues that are also subject to a nudge-based approach are marriage, education, eating, drinking, smoking, influence, spread of information, and politics. There is something in this book for everyone.The basis for this libertarian paternalism concept is in the social theory called "science of choice", the study of the design and implementation of influence systems on various kinds of people. The terms Econs and Humans, are used to refer to people with either considerable or little rational decision-making talent, respectively. The various libertarian paternalism concepts and systems presented are tested and compared in light of these two types of people. Two foundational issues that this book has in common with another book, Network of Echoes: Imitation, Innovation and Invisible Leaders, that was also reviewed for this issue of the Journal are that 1 ) there are two modes of thinking (or components of the brain) - an automatic (intuitive) process and a reflective (rational) process and 2) the need for conformity and the desire for imitation are powerful forces in human behavior. …

Nudge: Improving Decisions about Health, Wealth, and Happiness

Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

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General data protection regulation

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

A Survey on Conversational Recommender Systems

Recommender systems have become a popular and effective means to manage the ever-increasing amount of multimedia content available today and to help users discover interesting new items. Today’s recommender systems suggest items of various media types, including audio, text, visual (images), and videos. In fact, scientific research related to the analysis of multimedia content has made possible effective content-based recommender systems capable of suggesting items based on an analysis of the features extracted from the item itself. The aim of this survey is to present a thorough review of the state-of-the-art of recommender systems that leverage multimedia content, by classifying the reviewed papers with respect to their media type, the techniques employed to extract and represent their content features, and the recommendation algorithm. Moreover, for each media type, we discuss various domains in which multimedia content plays a key role in human decision-making and is therefore considered in the recommendation process. Examples of the identified domains include fashion, tourism, food, media streaming, and e-commerce.

Recommender Systems Leveraging Multimedia Content

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbe2.139

Parent distraction with phones, reasons for use, and impacts on parenting and child outcomes: A review of the emerging research

Digital overuse on mobile devices is a growing problem in everyday life. This paper describes a generalizable mobile intervention that combines nudge theory and negative reinforcement to create a subtle, repeating phone vibration that nudges a user to reduce their digital consumption. For example, if a user has a daily Facebook limit of 30 minutes but opens Facebook past this limit, the user's phone will issue gentle vibrations every five seconds, but the vibration stops once the user navigates away from Facebook. We evaluated the intervention through a three-week controlled experiment with 50 participants on Amazon's Mechanical Turk platform with findings that show daily digital consumption was successfully reduced by over 20%. Although the reduction did not persist after the intervention was removed, insights from qualitative feedback suggest that the intervention made participants more aware of their app usage habits; and we discuss design implications of episodically applying our intervention in specific everyday contexts such as education, sleep, and work. Taken together, our findings advance the HCI community's understanding of how to curb digital overload.

https://dl.acm.org/doi/pdf/10.1145/3229434.3229463

Good vibrations: can a digital nudge reduce digital overload?

Voice interfaces introduced by smart speakers present new opportunities and challenges for podcast content recommendations. Understanding how users interact with voice-based recommendations has the potential to inform better design of vocal recommenders. However, existing knowledge about user behavior is mostly for visual interfaces, such as the web, and is not directly transferable to voice interfaces, which rely on user listening and do not support skimming and browsing. To fill in the gap, we conducted a controlled study to compare user interactions with recommendations delivered visually to those with recommendations delivered vocally. Through an online A/B testing with 100 participants, we found that when recommendations are vocally conveyed, users consume more slowly, explore less, and choose fewer long-tail items. The study also reveals the correlation between user choices and exploration via voice interfaces. Our findings pose challenges to the design of voice interfaces, such as adaptively recommending diverse content and designing better navigation mechanisms.

https://dl.acm.org/doi/pdf/10.1145/3240323.3240389

Understanding user interactions with podcast recommendations delivered via voice

Behavior change is one of the most important problems faced by people and researchers today. Behavioral researchers have begun adopting smartphones as data-collection tools in psychological and behavioral science because these devices can study people in their everyday life, objectively measure behavior (using mobile sensing), and implement interventions. From a literature review of recent research on mobile behavior change, we identify three design components: mobile sensing, user contexts, and digital nudges. Informed by these components, we designed three example mobile research applications and propose a solution-focused, conceptual framework for deploying behavior change studies using mobile phones. We discuss future directions for research in psychological and behavioral science as these fields embrace mobile technology.

Towards A Framework for Mobile Behavior Change Research

Traditionally, recommendation systems are built on the assumption that each service provider has full access to all user data generated on its platform. However, with increasing data privacy concerns and personal data protection regulation, service providers such as Google, Twitter, and Facebook are enabling their users to revisit, erase, and rectify their historical profiles. Future recommendation systems need to be robust to such profile modifications and user-controlled data filtering. In this paper, we explore how recommendation performance may be affected by time-sensitive user data filtering, that is, users choosing to share only recent "N days" of data. Using the MovieLens dataset as a testbed, we evaluated three widely used collaborative filtering algorithms. Our experiments demonstrate that filtering out historical user data does not significantly affect the overall recommendation performance, but its impact on individual users may vary. These findings challenge the common belief that more data is essential to better performance, and suggest a potential win-win solution for services and end users.

https://dl.acm.org/doi/pdf/10.1145/3240323.3240399

Exploring recommendations under user-controlled data filtering

People's content choices are ideally driven by their intentions, aspirations, and plans. However, in reality, choices may be modulated by recommendation systems which are typically trained to promote popular items and to reinforce users' historical behavior. As a result, the utility and user experience of content consumption can be affected implicitly and undesirably. To study this problem, we conducted a 2 × 2 randomized controlled field experiment (105 urban college students) to compare the effects of intention informed recommendations with classical intention agnostic systems. The study was conducted in the context of spoken word web content (podcasts) which is often consumed through subscription sites or apps. We modified a commercial podcast app to include (1) a recommender that takes into account users' stated intentions at onboarding, and (2) a Collaborative Filtering (CF) recommender during daily use. Our study suggests that: (1) intention-aware recommendations can significantly raise users' interactions (subscriptions and listening) with channels and episodes related to intended topics by over 24%, even if such a recommender is only used during onboarding, and (2) the CF-based recommender doubles users' explorations on episodes from not-subscribed channels and improves satisfaction for users onboarded with the intention-aware recommender.

Michael Sobolev

Papers

Good vibrations: can a digital nudge reduce digital overload?

Understanding user interactions with podcast recommendations delivered via voice

Towards A Framework for Mobile Behavior Change Research

Exploring recommendations under user-controlled data filtering

How Intention Informed Recommendations Modulate Choices: A Field Study of Spoken Word Content