PICAE – Intelligent publication of audiovisual and editorial contents

The development in internet infrastructure and technology in last tow decades have given users and retailers the possibility to purchase and sell items online. This has of course broadened the horizons of what products can be offered outside of the traditional trading sense, to the point where virtually any product can be offered. These massive online markets have had a considerable impact on the habits of consumers, providing them access to a greater variety of products and information on these goods. This variety has made online commerce into a multi-billion dollar industry but it has also put the customer in a position where it is getting increasingly difficult to select the products that best fit their individual needs. In the same vein, the rise of both availability and the amounts of data that computers have been able to process in the last decades have allowed for many solutions that are computationally expensive to exist, and recommender systems are no exception. These systems are the perfect tools to overcome the information overload problem since they provide automated and personalized suggestions to consumers. The PICAE project tackles the recommendation problem in the audiovisual sector. The vast amount of audiovisual content that is available nowadays to the user can be overwhelming, which is why recommenders have been increasingly growing in popularity in this sector ---Netflix being the biggest example. PICAE seeks to provide insightful and personalized recommendations to users in a public TV setting. The PICAE project develops new models and analytical tools for recommending audiovisual and editorial content with the aim of improving the user experience, based on their profile and environment, and the level of satisfaction and loyalty. These new tools represent a qualitative improvement in the state of the art of television and editorial content recommendation. On the other hand, the project also improves the digital consumption index of these contents based on the identification of products that these new forms of consumption demand and how they must be produced, distributed and promoted to respond to the needs of this emerging market. The main challenge of the PICAE project is to resolve two differentiating aspects with respect to other existing solutions such as: variety and dynamic contents that requires a real-time analysis of the recommendation and the lack of available information about the user, who in these areas is reluctant to register, making it difficult to identify in multi-device consumption. This document will explain the contributions made in the development of the project, which can be divided in two: the development of the project, which can be divided in two: the development of a recommender system that takes into account information of both users and items and a deep analysis of the current metrics used to assess the performance of a recommender system

The Behavioral Code:Recommender Systems and the Technical Code of Behaviorism

Our lives are increasingly mediated, regulated and produced byalgorithmically-driven software; often invisible to the people whose lives it affects.Online, much of the content that we consume is delivered to us through algorithmic recommender systems (“recommenders”). Although the techniques of such recommenders and the specifc algorithms that underlie them differ, they share one basic assumption: that individuals are “users” whose preferences can be predicted through past actions and behaviors. While based on a set of assumptions that may be largely unconscious and even uncontroversial, we draw upon Andrew Feenberg’s work to demonstrate that recommenders embody a “formal bias” that has social implications. We argue that this bias stems from the “technical code” of recommenders – which we identify as a form of behaviorism. Studying the assumptions and worldviews that recommenders put forth tells us something about how human beings are understood in a time where algorithmic systems are ubiquitous. Behaviorism, we argue, forms the episteme that grounds the development of recommenders. What we refer to as the “behavioral code” of recommenders promotes an impoverished view of what it means to be human. Leaving this technical code unchallenged prevents us from exploring alternative, perhaps more inclusive and expansive, pathways for understanding individuals and their desires. Furthermore, by problematizing formations that have successfully rooted themselves in technical codes, this chapter extends Feenberg’s critical theory of technology into a domain that is both ubiquitous and undertheorized

Recommender systems in industrial contexts

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201

Personality representation: predicting behaviour for personalised learning support

The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles.This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods.This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support?Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services.The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach.The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support.The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming

Personalization in Social Media: Challenges and Opportunities for Democratic Societies

Personalization algorithms perform a fundamental role of knowledge management in order to restrain information overload, reduce complexity and satisfy individuals. Personalization of media content in mainstream social media, however, can be used for micro-target political messages, and can also create filter bubbles and strengthen echo chambers that restrain the exposure to diverse, challenging and serendipitous information. These represent fundamental issues for media law and ethics both seeking to preserve autonomy of choice and media pluralism in democratic societies. As a result, informational empowerment may be reduced and group polarization, audience fragmentation, conspiratorial thinking and other democratically negative consequences could arise. Even though research about the detrimental effects of personalization is more often inconsistent, there is no doubt that in the long run the algorithmic capacity to steer our lives in increasingly sophisticated ways will dramatically expand. Key questions need to be further discussed; for instance, to what extent can profiling account for the complexity of individual identity? To what extent are users, media and algorithms responsible in such practices? What are the main values and trade-offs that inform designers in such a fundamental societal algorithmic arbitrage? How is social media’s personalization directly or indirectly regulated in the European Union? The thesis firstly presents a critical overview of information societies, analyzing social media content personalization practices, dynamics and unintended consequences. Secondly, it explores the role of serendipity as a design and ethical principle for social media. Thirdly, the European legal landscape with regard to personalization is analyzed from a regulatory, governance and ethical perspective. Finally, it is introduced the concept of ‘algorithmic sovereignty’ as a valuable abstraction to begin to frame technical, legal and political preconditions and standards to preserve users’ autonomy, and to minimize the risks arising in the context of personalization

PROFILING - CONCEPTS AND APPLICATIONS

Profiling is an approach to put a label or a set of labels on a subject, considering the characteristics of this subject. The New Oxford American Dictionary defines profiling as: “recording and analysis of a person’s psychological and behavioral characteristics, so as to assess or predict his/her capabilities in a certain sphere or to assist in identifying a particular subgroup of people”. This research extends this definition towards things demonstrating that many methods used for profiling of people may be applied for a different type of subjects, namely things. The goal of this research concerns proposing methods for discovery of profiles of users and things with application of Data Science methods. The profiles are utilized in vertical and 2 horizontal scenarios and concern such domains as smart grid and telecommunication (vertical scenarios), and support provided both for the needs of authorization and personalization (horizontal usage).:The thesis consists of eight chapters including an introduction and a summary. First chapter describes motivation for work that was carried out for the last 8 years together with discussion on its importance both for research and business practice. The motivation for this work is much broader and emerges also from business importance of profiling and personalization. The introduction summarizes major research directions, provides research questions, goals and supplementary objectives addressed in the thesis. Research methodology is also described, showing impact of methodological aspects on the work undertaken. Chapter 2 provides introduction to the notion of profiling. The definition of profiling is introduced. Here, also a relation of a user profile to an identity is discussed. The papers included in this chapter show not only how broadly a profile may be understood, but also how a profile may be constructed considering different data sources. Profiling methods are introduced in Chapter 3. This chapter refers to the notion of a profile developed using the BFI-44 personality test and outcomes of a survey related to color preferences of people with a specific personality. Moreover, insights into profiling of relations between people are provided, with a focus on quality of a relation emerging from contacts between two entities. Chapters from 4 to 7 present different scenarios that benefit from application of profiling methods. Chapter 4 starts with introducing the notion of a public utility company that in the thesis is discussed using examples from smart grid and telecommunication. Then, in chapter 4 follows a description of research results regarding profiling for the smart grid, focusing on a profile of a prosumer and forecasting demand and production of the electric energy in the smart grid what can be influenced e.g. by weather or profiles of appliances. Chapter 5 presents application of profiling techniques in the field of telecommunication. Besides presenting profiling methods based on telecommunication data, in particular on Call Detail Records, also scenarios and issues related to privacy and trust are addressed. Chapter 6 and Chapter 7 target at horizontal applications of profiling that may be of benefit for multiple domains. Chapter 6 concerns profiling for authentication using un-typical data sources such as Call Detail Records or data from a mobile phone describing the user behavior. Besides proposing methods, also limitations are discussed. In addition, as a side research effect a methodology for evaluation of authentication methods is proposed. Chapter 7 concerns personalization and consists of two diverse parts. Firstly, behavioral profiles to change interface and behavior of the system are proposed and applied. The performance of solutions personalizing content either locally or on the server is studied. Then, profiles of customers of shopping centers are created based on paths identified using Call Detail Records. The analysis demonstrates that the data that is collected for one purpose, may significantly influence other business scenarios. Chapter 8 summarizes the research results achieved by the author of this document. It presents contribution over state of the art as well as some insights into the future work planned

Multi-dimensional clustering in user profiling

User profiling has attracted an enormous number of technological methods and applications. With the increasing amount of products and services, user profiling has created opportunities to catch the attention of the user as well as achieving high user satisfaction. To provide the user what she/he wants, when and how, depends largely on understanding them. The user profile is the representation of the user and holds the information about the user. These profiles are the outcome of the user profiling. Personalization is the adaptation of the services to meet the user’s needs and expectations. Therefore, the knowledge about the user leads to a personalized user experience. In user profiling applications the major challenge is to build and handle user profiles. In the literature there are two main user profiling methods, collaborative and the content-based. Apart from these traditional profiling methods, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, the profiling, achieved through these works, is lacking in terms of accuracy. This is because, all information within the profile has the same influence during the profiling even though some are irrelevant user information. In this thesis, a primary aim is to provide an insight into the concept of user profiling. For this purpose a comprehensive background study of the literature was conducted and summarized in this thesis. Furthermore, existing user profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these algorithms for user profiling was examined. A number of classification and clustering algorithms, such as Bayesian Networks (BN) and Decision Trees (DTs) have been simulated using user profiles and their classification accuracy performances were evaluated. Additionally, a novel clustering algorithm for the user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed. The MDC is a modified version of the Instance Based Learner (IBL) algorithm. In IBL every feature has an equal effect on the classification regardless of their relevance. MDC differs from the IBL by assigning weights to feature values to distinguish the effect of the features on clustering. Existing feature weighing methods, for instance Cross Category Feature (CCF), has also been investigated. In this thesis, three feature value weighting methods have been proposed for the MDC. These methods are; MDC weight method by Cross Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC) and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of these weighted MDC algorithms have been tested and evaluated. Additional simulations were carried out with existing weighted and non-weighted IBL algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user profiling to improve personalized service provisioning in mobile environments. The experiments presented in this thesis were conducted by using user profile datasets that reflect the user’s personal information, preferences and interests. The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA (version 3.5.7) machine learning platform. WEKA serves as a workbench to work with a collection of popular learning schemes implemented in JAVA. In addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life scenario is implemented as a Java Mobile Application (Java ME) on NetBeans IDE 7.1. All simulation results were evaluated based on the error rate and accuracy

Responsible innovation in mobile journalism : Exploring professional journalists` learning and innovation processes

Denne avhandlingen handler om innovasjon i mobiljournalistikk, og utforsker hvordan profesjonelle TV- og avisjournalister bruker smarttelefoner som journalistisk produksjonsverktøy. I tillegg reflekteres kritisk over utfordringer som kan knyttes til at journalister satser i sitt arbeid på datateknologi som ikke bare integrerer flere risikoteknologier men bygger på infrastrukturer som er optimalisert for omfattende dataekstraksjon og kommersielle overvåkingspraksiser. Det overordnete spørsmålet som søkes besvart i avhandlingen er: Hva er ansvarlig innovasjon i mobiljournalistikk? For å finne svar på forskningsspørsmålet kombineres empiriske tilnærminger og analytisk-teoretiske perspektiver. Innovasjon forstås her som en kompleks sosiokulturell læringsprosess der ´ansvarlig innovasjon´ pekes ut som en normativ meta-kategori. I den empiriske delen i avhandlingen undersøkes profesjonelle journalisters konkrete lærings- og innovasjonsprosesser. Basert på etnografi-inspirerte metoder som deltakende observasjon, dybdeintervjuer og uformelle samtaler belyser den empiriske delen av avhandlingen innovasjon i mobiljournalistikk gjennom to ulike casestudier. I den første casen utforskes et globalt pioner-nettverk som fremstår som en viktig kollektiv aktør i innovativ mobiljournalistikk. I den andre casen undersøkes et konkret trainingsarrangement for profesjonelle avisjournalister som ledd i en omfattende innovasjonsprosess i en tradisjonell medieorganisasjon. Den analytisk-teoretiske delen av avhandlingen tar for seg meta-konseptet `ansvarlig innovasjon´ og belyser kritisk den politiske økonomien knyttet til lærings- og kunnskapsutvikling. Ved hjelp av Zuboffs (2019) teori om overvåkningskapitalisme fokuserer denne delen av avhandlingen på større og mer langsiktige samfunnskonsekvenser knyttet til bruk av mobilteknologi i journalistikk. Ved å peke på ulike risikoer ved uregulerte former for datainnsamling og utfordringer knyttet til privatisering av kunnskap og kunnskapsproduksjon omhandler den teoretisk-analytiske delen hva som står på spill for journalister, medieorganisasjoner og samfunnet i sin helhet når mobilteknologi blir tatt ukritisk i bruk. Det konkluderes med at en uansvarlig og risikofylt bruk av mobilteknologi og relaterte infrastrukturer ikke tegner et bilde av mobiljournalistikk som en demokratiserende kraft (og tidsriktig produksjonsmåte) men heller en praksis som kan bidra til å undergrave demokratiets fundamenter gjennom omfattende dataekstraksjon og kommersielt motiverte overvåkningspraksiser. For å møte komplekse risikoer ved bruk av teknologisk innovasjon i mobiljournalistikk og å kunne finne konstruktive løsninger diskuteres det nye europeiske forsknings- og innovasjonsrammeverket Responsible Research and Innovation (RRI) som sikter mot grunnleggende endringer i nåværende innovasjons- og forskningspraksis. Med utgangspunkt i idéer og metoder fra RRI foreslås ulike handlingsopsjoner på individ-, organisasjonps- og samfunnsnivå samt anbefalinger hva `ansvarlig innovasjon i mobiljournalistikk` innebærer. Et overordnet mål med avhandlingen er å bidra i, og berike, den akademiske og offentlige debatten ved å gi konkrete innblikk i profesjonelle journalisters læringssituasjoner og innovasjonsprosesser og gjennom den rette oppmerksomheten mot fundamentale utfordringer ved bruk av kompleks datateknologi og infrastrukturer i samfunnet.This thesis examines innovation in the field of mobile journalism by examining how professional broadcast and print journalists learn about and adopt mobile technology for their journalistic practice and by investigating critically the side effects from journalists’ adoption of mobile computing platforms, encompassing highly convergent and different risk technologies. The overarching research question that guided this work asked: What is responsible innovation in mobile journalism? To find answers to this overarching research endeavor, I applied an approach that combines empirical and analytical-conceptual perspectives. Innovation is conceptualized in this work as a complex sociocultural process of learning, and responsible innovation is viewed as a meta-category of innovation. The empirical part sets out to understand actual learning practices and innovation processes by examining how professional print and broadcast journalists learn to adopt mobile technology and innovate through mobile journalism in different social settings. Based on a qualitative approach that applies methods such as long-term observations, participant observation, in-depth interviews, and informal conversations, the empirical part of the thesis provides insight into professional journalists’ individual motivations and experiences, organizational and new collective approaches to innovation, and learning processes. The conceptual part of the thesis examines the meta-concept of “responsible innovation” more closely by applying a critical perspective of political economy on learning and knowledge processes. Viewed through the lens of Zuboff’s (2019) surveillance capitalism theory, this part of the thesis draws attention to broader societal consequences attached to the adoption of mobile technology in journalism. By uncovering emerging risks and challenges from unregulated dataveillance and privatization of knowledge, this part demonstrates what is at stake if mobile technology is irresponsibly adopted by a risk group – in this case, journalists – and how, from this perspective, mobile journalism fails to emerge as a democratic force, thereby undermining the fundaments of democracy. To counteract the identified and complex risks from comprehensive data extraction and dataveillance that accompany journalists and media organizations’ adoption of and innovation in mobile journalism, ideas and methods from the European Union’s Responsible Research and Innovation framework are suggested as a possible approach. This is specified by outlining different implications from the identified risks on individual, organizational, and societal levels, and by making suggestions as to what “responsible innovation” in mobile journalism would encompass in the context of this thesis. This thesis aims to build on existing academic discussions through enriching debates in the mobile journalism field by providing insights into professional journalists’ concrete learning and innovation processes, as well as directing attention toward individual, organizational, and societal risks attached to uncritical adoption of a complex and pervasive computing platform in journalism practice and innovation in the field.Doktorgradsavhandlin

Digital participation : An exploration of how video conferencing impacts on criminal trials

We are at the international precipice of change in how people typically participate in criminal trials. From the traditional copresence of legal professionals, defendants, plaintiffs and witnesses in physical courtrooms, we are rapidly moving towards digital participation becoming more routine as reflected in the expeditious increase in the use of video conferencing in trials in Sweden and many other countries. However, whilst technological advances and legal rulings are enabling this digital shift, academic attention has failed to keep abreast of how participating in criminal trials by video conference is experienced by those taking part, or how this format of participation changes how they are perceived. Relatedly, the shift from participating in a physical legal setting to taking part via video link also has repercussions for conveying and upholding the legitimacy of legal proceedings. There is a risk the COVID-19 pandemic rushed the courtrooms into a digital world without appropriate investigation. This paper will discuss the extant research and present a project proposal that is centred around three research questions: How does participation by video conference change the experience of a legal trial? How is the ceremonial setting of a trial conveyed in video conferences? How does video conferencing impact on judicial evaluations of credibility and guilt? A combined qualitative and quantitative approach will be used. The empirical focus will be on criminal trials at district court concerning crimes against persons where credibility is of particular importance. The findings will produce new knowledge regarding the interpretations and practices of digital participation in legal trials and will also have important implications for the execution of justice beyond the site of study