Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

Tesis por compendioEn el día a día, las personas suelen confiar en recomendaciones, tradicionalmente aportadas por otras personas (familia, amigos, etc.) para sus decisiones más variadas. En el mundo digital esto no es diferente, dado que los sistemas de recomendación están presentes en todas partes y de modo transparente. El principal objetivo de estos sistemas es el de ayudar en el proceso de toma de decisiones, generando recomendaciones de su interés y basadas en sus gustos. Dichas recomendaciones van desde productos en sitios web de comercio electrónico, como libros o lugares a visitar, además de qué comer o cuánto tiempo uno debe caminar al día para tener una vida sana, con quién salir o a quién seguir en las redes sociales. Esta es un área en ascensión. Por un lado, tenemos cada vez más usuarios en internet cuya vida está digitalizada, dado que lo que se hace en el "mundo real" está representado en cierto modo en el "mundo digital". Por otro lado, sufrimos una sobrecarga de información, que puede mitigarse mediante el uso de un sistema de recomendación. Sin embargo, estos sistemas también enfrentan algunos problemas, como el problema del arranque en frío y su necesidad de ser cada vez más "humanos", "personalizados" y "precisos" para satisfacer las exigencias de usuarios y empresas. En este desafiante escenario, los sistemas de recomendación basados en la personalidad se están estudiando cada vez más, ya que son capaces de enfrentar esos problemas. Algunos proyectos recientes proponen el uso de la personalidad humana en los recomendadores, ya sea en su conjunto o individualmente por rasgos. Esta tesis está dedicada a este nuevo área de recomendación basada en la personalidad, centrándose en uno de sus rasgos más importantes, la curiosidad. Además, para explotar la información ya existente en internet, obtendremos de forma implícita información de las redes sociales. Por lo tanto, este trabajo tiene como objetivo proporcionar una mejor experiencia al usuario final a través de un nuevo enfoque que ofrece una alternativa a algunos de los retos identificados en los sistemas de recomendación basados en la personalidad. Entre estas mejoras, el uso de las redes sociales para alimentar los sistemas de recomendación reduce el problema del arranque en frío y, al mismo tiempo, proporciona datos valiosos para la predicción de la personalidad humana. Por otro lado, la curiosidad no ha sido utilizada por ninguno de los sistemas de recomendación estudiados; casi todos han usado la personalidad general de un individuo a través de los Cinco Grandes rasgos de la personalidad. Sin embargo, los estudios psicológicos confirman que la curiosidad es un rasgo relevante en el proceso de elegir un item, cuestión directamente relacionada con los sistemas de recomendación. En resumen, creemos que un sistema de recomendación que mida implícitamente la curiosidad y la utilice en el proceso de recomendar nuevos ítems, especialmente en el sector turístico, podría claramente mejorar la capacidad de estos sistemas en términos de precisión, serendipidad y novedad, permitiendo a los usuarios obtener niveles positivos de satisfacción con las recomendaciones. Esta tesis realiza un estudio exhaustivo del estado del arte, donde destacamos trabajos sobre sistemas de recomendación, la personalidad humana desde el punto de vista de la psicología tradicional y positiva y finalmente cómo se combinan ambos aspectos. Luego, desarrollamos una aplicación en línea capaz de extraer implícitamente información del perfil de usuario en una red social, generando predicciones de uno o más rasgos de su personalidad. Finalmente, desarrollamos el sistema CURUMIM, capaz de generar recomendaciones en línea con diferentes propiedades, combinando la curiosidad y algunas características sociodemográficas (como el nivel de educación) extraídas de Facebook. El sistema ha sido probado y evaluado en el contexto turístico por usuarios rEn el dia a dia, les persones solen confiar en recomanacions, tradicionalment aportades per altres persones (família, amics, etc.) per a les seues decisions més variades. En el món digital això no és diferent, atès que els sistemes de recomanació estan presents a tot arreu i de manera transparent. El principal objectiu d'aquests sistemes és el d'ajudar en el procés de presa de decisions, generant recomanacions del seu interès i basades en els seus gustos. Aquestes recomanacions van des de productes en pàgines web de comerç electrònic, com a llibres o llocs a visitar, a més de què menjar o quant temps una persona ha de caminar al dia per a tindre una vida sana, amb qui eixir o a qui seguir en les xarxes socials. Aquesta és una àrea en ascensió. D'una banda, tenim cada vegada més usuaris en internet la vida de les quals està digitalitzada, atès que el que es fa en el "món real" està representat en certa manera en el "món digital". D'altra banda, patim una sobrecàrrega d'informació, que pot mitigar-se mitjançant l'ús d'un sistema de recomanació. No obstant això, aquests sistemes també enfronten alguns problemes, com el problema de l'arrencada en fred i la seua necessitat de ser cada vegada més "humans", "personalitzats" i "precisos" per a satisfer les exigències d'usuaris i empreses. En aquest desafiador escenari, els sistemes de recomanació basats en la personalitat s'estan estudiant cada vegada més, ja que són capaços d'enfrontar eixos problemes. Alguns projectes recents proposen l'ús de la personalitat humana en els recomendadors, ja siga en el seu conjunt o individualment per trets. Aquesta tesi està dedicada a aquest nou àrea de recomanació basada en la personalitat, centrant-se en un dels seus trets més importants, la curiositat. A més, per a explotar la informació ja existent en internet, obtindrem de forma implícita informació de les xarxes socials. Per tant, aquest treball té com a objectiu proporcionar una millor experiència a l'usuari final a través d'un nou enfocament que ofereix una alternativa a alguns dels reptes identificats en els sistemes de recomanació basats en la personalitat. Entre aquestes millores, l'ús de les xarxes socials per a alimentar els sistemes de recomanació redueix el problema de l'arrencada en fred i, al mateix temps, proporciona dades valuoses per a la predicció de la personalitat humana. D'altra banda, la curiositat no ha sigut utilitzada per cap dels sistemes de recomanació estudiats; quasi tots han usat la personalitat general d'un individu a través dels Cinc Grans trets de la personalitat. No obstant això, els estudis psicològics confirmen que la curiositat és un tret rellevant en el procés de triar un item, qüestió directament relacionada amb els sistemes de recomanació. En resum, creiem que un sistema de recomanació que mesure implícitament la curiositat i la utilitze en el procés de recomanar nous ítems, especialment en el sector turístic, podria clarament millorar la capacitat d'aquests sistemes en termes de precisió, sorpresa i novetat, permetent als usuaris obtindre nivells positius de satisfacció amb les recomanacions. Aquesta tesi realitza un estudi exhaustiu de l'estat de l'art, on destaquem treballs sobre sistemes de recomanació, la personalitat humana des del punt de vista de la psicologia tradicional i positiva i finalment com es combinen tots dos aspectes. Després, desenvolupem una aplicació en línia capaç d'extraure implícitament informació del perfil d'usuari en una xarxa social, generant prediccions d'un o més trets de la seua personalitat. Finalment, desenvolupem el sistema CURUMIM, capaç de generar recomanacions en línia amb diferents propietats, combinant la curiositat i algunes característiques sociodemogràfiques (com el nivell d'educació) extretes de Facebook. El sistema ha sigut provat i avaluat en el context turístic per usuaris reals. Els resultats demostren la seua capacitat perIn daily life, people usually rely on recommendations, traditionally given by other people (family, friends, etc.) for their most varied decisions. In the digital world, this is not different, given that recommender systems are present everywhere in such a way that we no longer realize. The main goal of these systems is to assist users in the decision-making process, generating recommendations that are of their interest and based on their tastes. These recommendations range from products in e-commerce websites, like books to read or places to visit to what to eat or how long one should walk a day to have a healthy life, who to date or who one should follow on social networks. And this is an increasing area. On the one hand, we have more and more users on the internet whose life is somewhat digitized, given than what one does in the "real world" is represented in a certain way in the "digital world". On the other hand, we suffer from information overload, which can be mitigated by the use of recommendation systems. However, these systems also face some problems, such as the cold start problem and their need to be more and more "human", "personalised" and "precise" in order to meet the yearning of users and companies. In this challenging scenario, personality-based recommender systems are being increasingly studied, since they are able to face these problems. Some recent projects have proposed the use of the human personality in recommenders, whether as a whole or individually by facet in order to meet those demands. Therefore, this thesis is devoted to this new area of personality-based recommendation, focusing on one of its most important traits, the curiosity. Additionally, in order to exploit the information already present on the internet, we will implicitly obtain information from social networks. Thus, this work aims to build a better experience for the end user through a new approach that offers an option for some of the gaps identified in personality-based recommendation systems. Among these gap improvements, the use of social networks to feed the recommender systems soften the cold start problem and, at the same time, it provides valuable data for the prediction of the human personality. Another found gap is that the curiosity was not used by any of the studied recommender systems; almost all of them have used the overall personality of an individual through the Big Five personality traits. However, psychological studies confirm that the curiosity is a relevant trait in the process of choosing an item, which is directly related to recommendation systems. In summary, we believe that a recommendation system that implicitly measures the curiosity and uses it in the process of recommending new items, especially in the tourism sector, could clearly improve the capacity of these systems in terms of accuracy, serendipity and novelty, allowing users to obtain positive levels of satisfaction with the recommendations. This thesis begins with an exhaustive study of the state of the art, where we highlight works about recommender systems, the human personality from the point of view of traditional and positive psychology and how these aspects are combined. Then, we develop an online application capable of implicitly extracting information from the user profile in a social network, thus generating predictions of one or more personality traits. Finally, we develop the CURUMIM system, able to generate online recommendations with different properties, combining the curiosity and some sociodemographic characteristics (such as level of education) extracted from Facebook. The system is tested and assessed within the tourism context by real users. The results demonstrate its ability to generate novel and serendipitous recommendations, while maintaining a good level of accuracy, independently of the degree of curiosity of the users.Menk Dos Santos, A. (2018). Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/114798TESISCompendi

A scalable recommender system : using latent topics and alternating least squares techniques

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsA recommender system is one of the major techniques that handles information overload problem of Information Retrieval. Improves access and proactively recommends relevant information to each user, based on preferences and objectives. During the implementation and planning phases, designers have to cope with several issues and challenges that need proper attention. This thesis aims to show the issues and challenges in developing high-quality recommender systems. A paper solves a current research problem in the field of job recommendations using a distributed algorithmic framework built on top of Spark for parallel computation which allows the algorithm to scale linearly with the growing number of users. The final solution consists of two different recommenders which could be utilised for different purposes. The first method is mainly driven by latent topics among users, meanwhile the second technique utilises a latent factor algorithm that directly addresses the preference-confidence paradigm

Developing Digital Competences. Work learn trajectories in Italian School System

The work based learning is the core European dispositions on educational and training issue and a pillar of the Europe 2020 strategy (EUCOM 2009/C119/02). Therefore, the educational system has to increase the quality of standards and learning results in order to response adequately to competence needs and to permit the successful entrance of the youth in the world of work. The SWA is a coherent reaction. Indeed, the current literature lead to reflect on the SWA as a new prospective of school and world of work relationship (Arlotti and Barberis 2015), and as a resolution for the skills mismatch (Caputo and Capecchi 2016; Froy, Giguere, Hofer, 2009; A. Green, Hasluck, Hogarth, Reynolds, 2003). In a context which needs a different school that provides different types of skills, it is desirable that a policy instrument such as the SWA – became mandatory by the reform “La Buona Scuola” (Law 107/2015) – is included in the scientific debate, especially for its potential to contribute to renewal of the school system. Many authors encourage the scientific debate regarding the question to clarify the peculiar characteristics of the SWA model in Italy and to begin effective reflection on its revolutionary impact for the school system. According to Tino and Fideli (2015), the SWA is a process, not only as an experience, a fundamental methodology to promote the knowledge of the world of work and the development of competences (professional and citizenship) thanks to the interconnection between formal-informal learning and creative combination process between theory and practice

Re-examining and re-conceptualising enterprise search and discovery capability: towards a model for the factors and generative mechanisms for search task outcomes.

Many organizations are trying to re-create the Google experience, to find and exploit their own corporate information. However, there is evidence that finding information in the workplace using search engine technology has remained difficult, with socio-technical elements largely neglected in the literature. Explication of the factors and generative mechanisms (ultimate causes) to effective search task outcomes (user satisfaction, search task performance and serendipitous encountering) may provide a first step in making improvements. A transdisciplinary (holistic) lens was applied to Enterprise Search and Discovery capability, combining critical realism and activity theory with complexity theories to one of the worlds largest corporations. Data collection included an in-situ exploratory search experiment with 26 participants, focus groups with 53 participants and interviews with 87 business professionals. Thousands of user feedback comments and search transactions were analysed. Transferability of findings was assessed through interviews with eight industry informants and ten organizations from a range of industries. A wide range of informational needs were identified for search filters, including a need to be intrigued. Search term word co-occurrence algorithms facilitated serendipity to a greater extent than existing methods deployed in the organization surveyed. No association was found between user satisfaction (or self assessed search expertise) with search task performance and overall performance was poor, although most participants had been satisfied with their performance. Eighteen factors were identified that influence search task outcomes ranging from user and task factors, informational and technological artefacts, through to a wide range of organizational norms. Modality Theory (Cybersearch culture, Simplicity and Loss Aversion bias) was developed to explain the study observations. This proposes that at all organizational levels there are tendencies for reductionist (unimodal) mind-sets towards search capability leading to fixes that fail. The factors and mechanisms were identified in other industry organizations suggesting some theory generalizability. This is the first socio-technical analysis of Enterprise Search and Discovery capability. The findings challenge existing orthodoxy, such as the criticality of search literacy (agency) which has been neglected in the practitioner literature in favour of structure. The resulting multifactorial causal model and strategic framework for improvement present opportunities to update existing academic models in the IR, LIS and IS literature, such as the DeLone and McLean model for information system success. There are encouraging signs that Modality Theory may enable a reconfiguration of organizational mind-sets that could transform search task outcomes and ultimately business performance

Helping People and Places Move Out of Poverty: Progress and Learning 2010

Presents a mid-course review of a ten-year program to help the poor build economic security in the Southeast. Examines impact, lessons learned, factors that facilitated or impeded progress, how resources were leveraged, changing contexts, and next steps

Creating Context from Curiosity: The Role of Serendipity in the Research Process of Historians in Physical and Digital Environments

Serendipity, the word used to describe an unexpected encounter with information, people, or objects, has drawn much scholarly attention since its 1754 coinage by Horace Walpole. Historians commonly use this term when describing unexpected encounters during their research. However, historians have also been shown to be meticulous, organized researchers whose work is unlikely to contain elements that are unexpected. This thesis is an investigation of serendipity as it is recognized, defined, and experienced by historians in both physical and digital environments. Article One presents a grounded theory analysis of 20 interview transcriptions, Article Two presents a combination of grounded theory, content analysis, and narrative analysis of historians’ responses to an online survey, and Article Three summarizes the quantitative responses to the same survey, but focuses on digital environments. In Article One we found that historians frequently used active verbs to describe serendipity, and concluded that agency plays a prominent role in these experiences. In Article Two, responses from 142 participants reinforce the importance of agency, demonstrating that active research methods lead them to these serendipitous encounters. Article Three reports on the features of digital environments that historians found to support serendipity, including those that encourage exploration, connect people, have options for keyword searching, and highlight potentially relevant links. Taken together, these articles comprise a thesis that advances our current understanding of serendipity. Contributions to the field of LIS include acknowledging the role of agency in serendipitous encounters, and the use of multi-method analysis for investigating serendipity in a single population

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

The Ingenious Crowd�: A Critical Prosopography of British Inventors, 1650-1850

simulation, models

Influencing collaboration to enhance knowledge work through serendipity: user-study and design considerations

We all were strangers to someone at some point and that is the starting point to analyze unexpected encounters. The busy pace of life has alienated people from each other, hence, this created an opportunity for technology to support social experiences. Meeting new people that one would not normally encounter in the vicinity or in the regular social sphere would expand the opportunities for establishing connections. Connections that go beyond establishing friendship bonds, but finding collaborators for the development of projects. This thesis was developed in order to understand the concept of serendipity in the context of computational systems and how it can be used to facilitate encounters among knowledge workers. The analysis of this thesis is conceived within the borders of Human-Technology Interaction, using psychological and sociality approaches from a technological perspective that allows a better understanding of the people’s needs when developing tools to support social interactions. The theoretical chapters start analyzing the phenomenon of serendipity from different perspectives, along with concepts about knowledge work and matchmaking. In order to understand the phenomenon of serendipity, the term is defined from social perspectives to psychological ones. The purpose of this is to set the basic premises of the study and introduce how serendipity is approached in terms of computational systems and knowledge work. Then, it analyzes matchmaking and grouping by presenting knowledge networks, social matchmaking with professional purposes and context awareness. The user study is carried out by a set of interviews to participants in Demola (an ecosystem that joins students with projects from companies), followed by a comparison of different tools that already exist that help matchmaking. The purpose of the user study was to analyze manual matchmaking among strangers. It analyzes participants’ experiences when working with strangers to carry out different innovation projects. It also intends to determine the expectations when forming a group. Added to that, the head of Demola Tampere was interviewed to understand the manual matching participants process. The final chapter presents a set of considerations when designing for serendipity to enhance knowledge work. The conceptualization of serendipity and the user study are the basis for establishing a set of guidelines in design. Which intend to enhance matchmaking in knowledge workers by analyzing weak ties as a way of serendipity. This study emphasizes on the goals and expectations of the users when finding a professional partner. Based on the user study, a model is presented which shows a possible structure for matchmaking