Influences of Serendipity on Consumer Medical Information Personalization

Serendipity is an important concept in the field of information science. It has a potential of enhancing information seeking process by unexpected discovery. Serendipitous recommendation has been incorporated into the design of personalized systems to minimize blind spots in information delivery. Little evidence has been found to identify how serendipity influences personalization of consumer medical information delivery. This dissertation attempts to examine what roles serendipity plays in filtering consumer medical information and to understand how to incorporate serendipity in an effective manner. In addition, the study seeks to clarify user attitudes on unexpected discoveries of medical content in filtering settings as well as users' interest changes during this process. To empirically analyze the influence of serendipity, a medical news filtering system named MedSDFilter was developed. The system can personalize the delivery of news articles based on users' interest profiles. In MedSDFilter, serendipitous recommendation was integrated into personalized filtering through one of three serendipity models (randomness-based, knowledge-based and learning-based). Using Medical News Today site as information source, three different system modalities were compared by conducting user experiments. Thirty staff members were recruited to read and rate medical news delivered by one of three system modalities. The results of user study indicate that serendipity has an important role in medical news content delivery. As for how to incorporate serendipity, it is shown that using physician knowledge effectively enhanced serendipitous recommendation. In addition, the results suggest that the performance of serendipitous recommendation was further improved after learning algorithms were adopted. This study also provide some evidence to show user satisfaction on unexpected discovery and user interest change associated with this type of discovery. Finally, the study demonstrated the individual difference in seeking consumer medical information. The results of this study provide the system designers implications and suggestions to avoid potential drawbacks related to over-personalization in information delivery. This study enhances the understanding of users' behavior regarding the consumption of medical information and generates new guidelines which can be used in developing information systems in medical area.Doctor of Philosoph

Serendipitous News Discovery Increases News Consumption in News Recommender Systems

News recommender system users obtain news via incidental exposure to news and experience serendipity in the incidental news consumption. Serendipitous news discovery, the same as serendipity, refers to discovering unexpected and useful information unintentionally. Researchers suggest building serendipitous news recommender systems and increasing serendipitous news discovery to increase the diversity of the news consumption. However, the impacts of serendipitous news discovery on news consumption are uninvestigated, and rare research provides theoretical guidance to the serendipitous news recommender systems. The thesis investigated the impacts of serendipitous news discovery on news consumption with a serendipityrelated emotion, surprise, as a mediator and need for activation as a moderator. 463 participants recruited from Amazon MTurk completed the online survey-experiment. The findings suggest that surprise mediates the correlations between serendipitous news discovery and news consumption. Users who experience higher serendipitous news discovery indicate more positive attitudes on news consumption in the news recommender systems. The results also indicate the possibility that the lack of constant serendipitous news discovery may lead to the consumption of the news similar to the news that trigger serendipity. The research suggests that serendipitous news discovery increases news consumption, including news selection and reading

Information behaviour of humanities PhDs on an information literacy course

Purpose – The aim of this paper is to examine whether an information literacy course/module is an appropriate intervention during the initial months of a humanities PhD, and if there is more that can be learned from the course participants that might provide a better understanding of their information behaviour. Design/methodology/approach – A questionnaire was distributed to new humanities PhD students prior to their attending the course. A second questionnaire was distributed to those who had completed the course in full. Interviews were conducted with six participants to gain a richer understanding of how their information-seeking needs had evolved. Findings – Despite the relatively generic nature of the module, and the diversity of humanities research, the course had clear benefits for the participants. In their first year, scoping their topic and finding quality information can pose a challenge. The participants reported that the most appropriate time to attend the course is during the initial months. Some preferred to attend (or repeat) particular units later as workshops. The most valued elements were those that helped them bridge initial gaps. Face-to-face delivery is preferred. There is some potential for further one-to-one contact with librarians and additional follow-up workshops. Practical implications – This study can inform how librarians can better support PhD researchers in the humanities. Originality/value – The study is based around an established and accredited humanities PhD course that has already been adapted in various ways in terms of content and timing of delivery. Drawing on Kuhlthau's "Information Search Process", the study seeks a deeper understanding of a specific humanities group during the initial months of their PhD research

A food recipe recommendation system based on nutritional factors in the Finnish food communit

Abstract. This thesis presents a comprehensive study on the relationships between user feedback, recipe content, and additional factors in the context of a recipe recommendation system. The aim was to investigate the influence of various factors on user ratings and comments related to nutritional variables, while also exploring the potential for personalized recipe suggestions. Statistical analysis, clustering techniques, and sentiment analysis were employed to analyze a dataset of food recipes and user feedback. We determined that user feedback is a complex phenomenon influenced by subjective factors beyond recipe content alone. Cluster analysis identified four distinct clusters within the dataset, highlighting variations in nutritional values and sentiment among recipes. However, due to an imbalanced distribution within the clusters, these relationships were not considered in the recommendation system. To address the absence of user-related data, a content-based filtering approach was implemented, utilizing nutritional factors and a health factor calculation. The system provides personalized recipe recommendations based on nutritional similarity and health considerations. A maximum limit of 20 recommended recipes was set, allowing users to specify the desired number of recommendations. The accompanying API also provides a mean squared error metric to assess recommendation quality. This research contributes to a better understanding of user preferences, recipe content, and the challenges in developing effective recommendation systems for food recipes

Emerging Technology IS Course Design: Blockchain for Business Example

IS curricula require constant updating to accommodate the emergence of new technologies. Designing and delivering effective emerging technology courses within the constraints of existing programs remains an important challenge faculty face. This paper presents a template for approaching these courses from a learning theory perspective. Results of tests of this template, developed for teaching blockchain, indicate that it successfully strikes the balance needed in an IS program while simplifying the work of designing the structure of an emerging technology course. Additionally, this design was able to deliver this success in an online format, which can be a more challenging format for observing application of knowledge. Blockchain is a disruptive emerging technology opportunity for businesses to unlock value through trusted and “smart” peer-to-peer transactions, wherein smart means businesspeople can custom design processes for verification and transfer of assets. The blockchain example provided here includes a flexible 7-scenario design targeted to enable a constructive, project-based learning approach focused on authentic learning experiences. The template as applied to blockchain may be used directly or adapted for easier development of other emerging technology courses

EvoRecSys: Evolutionary framework for health and well-being recommender systems

Hugo Alcaraz-Herrera's PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnologia - CONACyT).In recent years, recommender systems have been employed in domains like ecommerce, tourism, and multimedia streaming, where personalising users’ experience based on their interactions is a fundamental aspect to consider. Recent recommender system developments have also focused on well-being, yet existing solutions have been entirely designed considering one single well-being aspect in isolation, such as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel recommendation framework that proposes evolutionary algorithms as the main recommendation engine, thereby modelling the problem of generating personalised well-being recommendations as a multi-objective optimisation problem. EvoRecSys captures the interrelation between multiple aspects of well-being by constructing configurable recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered. By instantiating the framework into an implemented model, we illustrate the use of a genetic algorithm as the recommendation engine. Finally, this implementation has been deployed as a Web application in order to conduct a users’ study.Consejo Nacional de Ciencia y Tecnologia (CONACyT

Supporting Serendipity through Interactive Recommender Systems in Higher Education

Serendipiteetin käsite viittaa onnekkaisiin sattumuksiin, jossa hyödyllistä tietoa tai muita arvokkaita asioita löydetään yllättäen. Suosittelujärjestelmien tutkimuksessa serendipiteetistä on tullut keskeinen kokemuksellinen tavoite. Ihmisen ja tietokoneen vuorovaikutuksen kannalta olennainen kysymys siitä, kuinka käyttöliittymäsuunnittelu suosittelujärjestelmissä voisi tukea serendipiteetin kokemusta, on kuitenkin saanut vain vähän huomiota. Tässä työssä tutkitaan, kuinka suosittelijajärjestelmän mahdollistamaa serendipiteetin kokemusta voidaan soveltaa tutkimusartikkelien suositteluihin korkeakouluopetuksen kontekstissa. Erityisesti työ tarkastelee suositusjärjestelmäsovellusten käyttöä kehittyvissä maissa, sillä suurin osa kehittyvissä maissa tehdyistä tutkimuksista on keskittynyt pelkästään järjestelmien toteutukseen. Tässä väitöskirjassa kuvataan suosittelujärjestelmien käyttöliittymien suunnittelua ja kehittämistä, tavoitteena ymmärtää paremmin serendipiteetin kokemuksen tukemista käyttöliittymäratkaisuilla. Tutkimalla näitä järjestelmiä kehittyvässä maassa (Pakistan), tämä väitöskirja asettaa suosittelujärjestelmien käytön vastakkain aikaisempien teollisuusmaissa tehtyjen tutkimusten kanssa, ja siten mahdollistaa suositusjärjestelmien soveltamiseen liittyvien kontekstuaalisten ja kulttuuristen haasteiden tarkastelua. Väitöskirja koostuu viidestä empiirisestä käyttäjätutkimuksesta ja kirjallisuuskatsausartikkelista, ja työ tarjoaa uusia käyttöliittymäideoita, avoimen lähdekoodin ohjelmistoratkaisuja sekä empiirisiä analyyseja suositusjärjestelmiin liittyvistä käyttäjäkokemuksista pakistanilaisessa korkeakoulussa. Onnekkaita löytöjä tarkastellaan liittyen tutkimusartikkelien löytämiseen suositusjärjestelmän avulla. Väitöstyö kattaa sekä konstruktiivista että kokeellista tutkimusta. Väitöskirjan artikkelit esittelevät alkuperäistä tutkimusta, jossa kokeillaan erilaisia käyttöliittymämalleja, pohditaan sidosryhmien vaatimuksia, arvioidaan käyttäjien kokemuksia suositelluista artikkeleista ja esitellään tutkimusta suositusjärjestelmien tehtäväkuormitusanalyysistä.Serendipity is defined as the surprising discovery of useful information or other valuable things. In recommender systems research, serendipity has become an essential experiential goal. However, relevant to Human-Computer Interaction, the question of how the user interfaces of recommender systems could facilitate serendipity has received little attention. This work investigates how recommender system-facilitated serendipity can be applied to research article recommendation processes in the context of higher education. In particular, this work investigates the use of recommender system applications in developing countries as most studies in developing countries have focused solely on implementation, rather than user experiences. This dissertation describes the design and development of several user interfaces for recommender systems in an attempt to improve our understanding of serendipity facilitation with the help of user interfaces. By studying these systems in a developing country, this dissertation contrasts the study of recommender systems in developed countries, examining the contextual and cultural challenges associated with the application of recommender systems. This dissertation consists of five empirical user studies and a literature review article, contributing novel user interface designs, open-source software, and empirical analyses of user experiences related to recommender systems in a Pakistani higher education institution. The fortunate discoveries of recommendations are studied in the context of exploring research articles with the help of a recommender system. This dissertation covers both constructive and experimental research. The articles included in this dissertation present original research experimenting with different user interface designs in recommender systems facilitating serendipity, discuss stakeholder requirements, assess user experiences with recommended articles, and present a study on task load analysis of recommender systems. The key findings of this research are that serendipity of recommendations can be facilitated to users with the user interface. Recommender systems can become an instrumental technology in the higher education research and developing countries can benefit from recommender systems applications in higher education institutions

When personalization is not an option: An in-the-wild study on persuasive news recommendation

Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user’s interest on suggested items leveraging the way news is perceived. We present the results of two evaluations “in the wild”, carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education