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Towards Explanations of Anti-Recommender Content in Public Radio
Abstract
Other than private broadcasters, publicly financed broadcasters have to fulfil a public service remit. Individual playouts in public radio, therefore, consist not only of recommender content but also of 'anti-recommender content" that matches public interests. Such anti-recommender content in individual playouts may be unexpected for users and may need explanation. To find out what explanations might look like in public radio, we elicit the requirements of the public service remit for an example country. Based on these requirements, we propose an approach for designing explanations of recommendations that align with the public service remit.
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Machine Learning (ML) models are increasingly being used in many sectors, ranging from health and education to justice and criminal investigation. Therefore, building a fair and transparent model which conveys the reasoning behind its predictions is of great importance. This chapter discusses the role of explanation mechanisms in building fair machine learning models and explainable ML technique. We focus on the special case of recommender systems because they are a prominent example of a ML model that interacts directly with humans. This is in contrast to many other traditional decision making systems that interact with experts (e.g. in the health-care domain). In addition, we discuss the main sources of bias that can lead to biased and unfair models. We then review the taxonomy of explanation styles for recommender systems and review models that can provide explanations for their recommendations. We conclude by reviewing evaluation metrics for assessing the power of explainability in recommender systems.
In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.
Die fortschreitende Digitalisierung des gesellschaftlichen Lebens erzeugt neben vielen anderen Phänomenen auch neue Logiken der Informationsvermittlung. So wird seit einiger Zeit die Problematik der Filter Bubble durch Personalisierung von Informationszugängen intensiv diskutiert. Algorithmische Systeme als Vermittler personalisierter Informationen, hier verstanden als Infomediaries (Morris 2015), können zu einer Zersplitterung von Öffentlichkeiten führen – Individuen werden verstärkt mit selektiven Informationen und Diskursen konfrontiert, welche die eigenen Haltungen bestärken und abweichende Meinungen ausblenden. Diese Transformation stellt vor dem Hintergrund der informationellen Grundversorgung gerade für den öffentlich-rechtlichen Rundfunk eine Herausforderung dar. Der vorliegende Beitrag nimmt dies zum Anlass, um die Beziehung und Möglichkeiten der Realisierung des Programmauftrags in Zeiten non-linearer Medienangebote zu diskutieren. Der Artikel ist ein erstes Ergebnis der Zusammenarbeit zwischen den Autoren und der Softwareentwicklungsabteilung des Bayerischen Rundfunks im Rahmen der Entwicklung einer neuen Mediathek. In einer kollaborativen Ethnographie wurden Rahmenbedingungen und Gestaltungsmöglichkeiten algorithmisch vermittelter Programme, welche dem gesellschaftlichen Auftrag der öffentlichrechtlichen Rundfunkanstalten genügen, erforscht. Entgegen der Annahme, Algorithmen seien unhinterfragbare und dadurch mächtige Akteure, zeigt sich, dass die Realisierung algorithmischer Systeme sowohl von sozio-technischen Aushandlungsprozessen als auch von infrastrukturellen und organisatorischen Bedingungen abhängt, und sich somit gerade in den Entwicklungsprozessen inhaltliche und politische Gestaltungsräume von Recommender Systemen
eröffnen.
Some fear that personalised communication can lead to information cocoons or filter bubbles. For instance, a personalised news website could give more prominence to conservative or liberal media items, based on the (assumed) political interests of the user. As a result, users may encounter only a limited range of political ideas. We synthesise empirical research on the extent and effects of self-selected personalisation, where people actively choose which content they receive, and pre-selected personalisation, where algorithms personalise content for users without any deliberate user choice. We conclude that at present there is little empirical evidence that warrants any worries about filter bubbles.
Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
Online news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency,
more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount
of news events might be released at a rate of hundreds, even thousands per hour. A challenging problem is how to effciently
select specific news articles from a large corpus of newly-published press releases to recommend to individual readers, where
the selected news items should match the reader's reading preference as much as possible. This issue refers to personalized
news recommendation. Recently, personalized news recommendation has become a promising research direction as the Internet
provides fast access to real-time information from multiple sources around the world. Existing personalized news recommendation
systems strive to adapt their services to individual users by virtue of both user and news content information. A variety
of techniques have been proposed to tackle personalized news recommendation, including content-based, collaborative filtering
systems and hybrid versions of these two. In this paper, we provide a comprehensive investigation of existing personalized
news recommenders. We discuss several essential issues underlying the problem of personalized news recommendation, and explore
possible solutions for performance improvement. Further, we provide an empirical study on a collection of news articles obtained
from various news websites, and evaluate the effect of different factors for personalized news recommendation. We hope our
discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.
Keywordsnews recommendation–personalization–scalability–user profiling–modeling–ranking
Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen.This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.
Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user's perspective. The goal of this research was to examine the role of transparency (user understanding of why a particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent.
Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to under-stand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior. We found that a substantial portion of our user base (25%) used the recommender-switching feature. The majority of users who used the control only switched algorithms a few times, trying a few out and settling down on an algorithm that they would leave alone. The largest number of users prefer a matrix factorization algorithm, followed closely by item-item collaborative filtering; users selected both of these algorithms much more often than they chose a non-personalized mean recommender. The algorithms did produce measurably different recommender lists for the users in the study, but these differences were not directly predictive of user choice.
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.
Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in col-laborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of ques-tions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
NPR Digital Media: lessons learned in creating and delivering a digital listening experience
- Z Brand
Z. Brand, "NPR Digital Media: lessons learned in creating and delivering a
digital listening experience," presented at the Radio 2.0 Keynote, Paris, 2015.
Wie Nutzer im Suchprozess gelenkt werden: Zwischen technischer Unterstützung und interessengeleiteter Darstellung
- D Lewandowski
D. Lewandowski, "Wie Nutzer im Suchprozess gelenkt werden: Zwischen
technischer
Unterstützung
und
interessengeleiteter
Darstellung,"
SearchStudies, 13-May-2014..