Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review

Background: Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population. Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, “Ensure healthy lives and promote well-being for all at all ages”), and to suggest possible reasons for these gaps as well as to propose some solutions. Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary taxonomy. Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and behavior change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not assess (cost)-effectiveness. Discussion: Health recommender systems may be further improved by using relevant behavior change strategies and by implementing essential characteristics of tailored interventions. In addition, many of the features required to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects were not reported in the studies. Conclusions: The studies analyzed presented few evidence in support of the positive effects of using health recommender systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with electronic health records and the incorporation of health promotion theoretical factors and behavior change theories. This will render those studies more useful for policymakers since they will cover all aspects needed to determine their impact toward meeting SDG3.European Union's Horizon 2020 No 68112

‎Provenance Based Trust Boosted Recommender System Using Boosted Vector Similarity Measure

‎As users in an online social network are overwhelmed by the abundant amount of information‎, ‎it is very hard to retrieve the preferred or required content‎. ‎In this context‎, ‎an online recommender system helps to filter and recommend content such as people,items or services‎. ‎But‎, ‎in a real scenario‎, ‎people rely more on recommendations‎ ‎from trusted sources than distrusting sources‎. ‎Though‎, ‎there are many trust based recommender systems that exist‎, ‎it lag in prediction error‎. ‎In order to improve the accuracy of the prediction‎, ‎this paper proposes a Trust-Boosted Recommender System (TBRS)‎. ‎Since‎, ‎the provenance derives the trust in a better way than other approaches‎, ‎TBRS is built‎ ‎from the provenance concept‎. ‎The proposed recommender system takes the provenance based fuzzy rules which were derived from the Fuzzy Decision Tree‎. ‎TBRS then computes the multi-attribute vector similarity score and boosts the score with trust weight‎. ‎This system is tested on the book-review dataset to recommend the top-k trustworthy reviewers.The performance of the proposed method is evaluated in terms of MAE and RMSE‎. ‎The result shows that the error value of boosted similarity is lesser than without boost‎. ‎The reduced error rates of the Jaccard‎, ‎Dice and Cosine similarity measures are 18\%‎, ‎15\% and 7\% respectively‎. ‎Also‎, ‎when the model is subjected to failure analysis‎, ‎it gives better performance for unskewed data than slewed data‎. ‎The models fbest‎, ‎average and worst case predictions are 90\%‎, ‎50\% and $<$23\% respectively‎

Prospective Student Information Booklet (1997-98)

Booklet containing curriculum and course information for future law students.https://ir.law.fsu.edu/prospective-student/1029/thumbnail.jp

Prospective Student Information Booklet (1997-98)

Booklet containing curriculum and course information for future law students.https://ir.law.fsu.edu/prospective-student/1029/thumbnail.jp

A Compositional Model of Multi-faceted Trust for Personalized Item Recommendation

Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. However, they are challenged by recent studies according to which people generally perceive the usage of data about social relations as a violation of their own privacy. In order to address this issue, we extend trust-based recommender systems with additional evidence about trust, based on public anonymous information, and we make them configurable with respect to the data that can be used in the given application domain: 1 - We propose the Multi-faceted Trust Model (MTM) to define trust among users in a compositional way, possibly including or excluding the types of information it contains. MTM flexibly integrates social links with public anonymous feedback received by user profiles and user contributions in social networks. 2 - We propose LOCABAL+, based on MTM, which extends the LOCABAL trust-based recommender system with multi-faceted trust and trust-based social regularization. Experiments carried out on two public datasets of item reviews show that, with a minor loss of user coverage, LOCABAL+ outperforms state-of-the art trust-based recommender systems and Collaborative Filtering in accuracy, ranking of items and error minimization both when it uses complete information about trust and when it ignores social relations. The combination of MTM with LOCABAL+ thus represents a promising alternative to state-of-the-art trust-based recommender systems

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

A Survey on Energy Efficiency in Smart Homes and Smart Grids

Empowered by the emergence of novel information and communication technologies (ICTs) such as sensors and high-performance digital communication systems, Europe has adapted its electricity distribution network into a modern infrastructure known as a smart grid (SG). The benefits of this new infrastructure include precise and real-time capacity for measuring and monitoring the different energy-relevant parameters on the various points of the grid and for the remote operation and optimization of distribution. Furthermore, a new user profile is derived from this novel infrastructure, known as a prosumer (a user that can produce and consume energy to/from the grid), who can benefit from the features derived from applying advanced analytics and semantic technologies in the rich amount of big data generated by the different subsystems. However, this novel, highly interconnected infrastructure also presents some significant drawbacks, like those related to information security (IS). We provide a systematic literature survey of the ICT-empowered environments that comprise SGs and homes, and the application of modern artificial intelligence (AI) related technologies with sensor fusion systems and actuators, ensuring energy efficiency in such systems. Furthermore, we outline the current challenges and outlook for this field. These address new developments on microgrids, and data-driven energy efficiency that leads to better knowledge representation and decision-making for smart homes and SGsThis research was co-funded by Interreg Österreich-Bayern 2014–2020 programme project KI-Net: Bausteine für KI-basierte Optimierungen in der industriellen Fertigung (AB 292). This work is also supported by the ITEA3 OPTIMUM project and ITEA3 SCRATCH project, all of them funded by the Centro Tecnológico de Desarrollo Industrial (CDTI), Spain