Science Recommendations on Social Media Platforms

Social media platforms can be a source of recommendations for various items or products. Digital Marketing takes advantage of this, especially when using influencers to evaluate their items or products on social media platforms. Each influencer can be a recommender of items or products. However, one can question why this does not occur similarly in the science area. This work presents a study based on comments shared on social media platforms about scientific documents (for example, journal or conference articles, thesis, reports, and patents). We pretend to obtain the polarity (positive, negative, and neutral) for each scientific comment. Most mentions are neutral and some considered positive, which allows us to conclude that there are shares of comments about science and that they can serve as the basis for a science recommendation system

Recomendações de Ciência em plataformas de redes sociais

As plataformas de redes sociais podem ser vistas como fontes de recomendações de variados itens ou produtos. O marketing aproveita ao máximo esta potencialidade sobretudo ao tirar proveito de influencers, que ao experimentarem esses itens fazem avaliações dos mesmos, isto pode ser visto como recomendações. No entanto, pode-se questionar porque é que na área da ciência isto não ocorre de uma forma semelhante. Este artigo apresenta um estudo em que se recolheram partilhas/menções em plataformas de redes sociais sobre documentos científicos (artigos de revistas ou conferências, relatórios, patentes, entre outros) e dessa recolha foi obtida a polaridade desses comentários (positivos, negativos e neutros. Verificou-se que a maior parte das menções são neutras e algumas consideradas positivas. Isto permite concluir que há partilhas de menções sobre ciência e que podem servir como base de um sistema de recomendação de ciência.Social media platforms can be a source of recommendations for various items or products. Digital Marketing takes advantage of this, especially when using influencers to evaluate their items or products on social media platforms. Each influencer can be a recommender of items or products. However, one can question why this does not occur similarly in the science area. This work presents a study based on comments shared on social media platforms about scientific documents (for example, journal or conference articles, thesis, reports, and patents). We pretend to obtain the polarity (positive, negative, and neutral) for each scientific comment. Most mentions are neutral and some considered positive, which allows us to conclude that there are shares of comments about science and that they can serve as the basis for a science recommendation system.Este trabalho foi financiado por fundos nacionais através da FCT - Fundação para a Ciência e Tecnologia no âmbito do Projeto Scope UIDB/00319/2020

Serendipitous recommendation for scholarly papers considering relations among researchers

10.1145/1998076.1998133Proceedings of the ACM/IEEE Joint Conference on Digital Libraries307-31

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they expect from the system. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. Besides, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists.We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing the users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” data sets to compare our recommendation results with some other standard baseline methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage and aggregate diversity, while avoiding any accuracy loss

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they expect from the system. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. Besides, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists.We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing the users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” data sets to compare our recommendation results with some other standard baseline methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage and aggregate diversity, while avoiding any accuracy loss

Elaboración de un Sistema de Recomendación de Publicaciones Científicas Nacionales de Acceso Abierto para los investigadores calificados del SINACYT

Actualmente existe un crecimiento sostenido sobre la producción científica mundial. Esta producción científica es preservada a través de repositorios de acceso abierto digitales, los cuales se crean como herramientas de apoyo para el desarrollo de producción científica. Sin embargo, existen deficiencias en la funcionalidad de los mismos como herramientas de apoyo para el aumento de la visibilidad, uso e impacto de la producción científica que albergan. El Perú, no es ajeno al crecimiento de la producción científica mundial. Con el avance del mismo, se implementaron nuevas plataformas (ALICIA y DINA) de difusión y promoción del intercambio de información entre las distintas instituciones y universidades locales. No obstante, estas plataformas se muestran como plataformas aisladas dentro del sistema científico-investigador, ya que no se encuentran integradas con las herramientas y procesos de los investigadores. El objetivo de este Proyecto es el de presentar una alternativa de solución para la resolución del problema de carencia de mecanismos adecuados para la visualización de la producción científica peruana a través de la implementación de un Sistema de Recomendación de Publicaciones Científicas Nacionales de Acceso Abierto para los investigadores calificados del SINACYT. Esta alternativa se basa en la generación de recomendaciones personalizadas de publicaciones en ALICIA, a través del uso del filtrado basado en contenido tomando en cuenta un perfil de investigador. Este perfil se construyó a partir de la información relevante sobre su producción científica publicada en Scopus y Orcid. La generación de recomendaciones se basó en la técnica de LSA (Latent Semantic Analysis), para descubrir estructuras semánticas escondidas sobre un conjunto de publicaciones científicas, y la técnica de Similitud Coseno, para encontrar aquellas publicaciones científicas con el mayor nivel de similitud. Para el Proyecto, se implementaron los módulos de extracción, en donde se recoge la data de las publicaciones en ALICIA y las publicaciones en Scopus y Orcid para cada uno de los investigadores registrados en DINA a través de la técnica de extracción de datos de sitios web (web scrapping); de pre procesamiento, en donde se busca la mejora de la calidad de la data previamente extraída para su posterior uso en el modelo analítico dentro del marco de la minería de texto; de recomendación, en donde se capacita un modelo LSA y se generan recomendaciones sobre qué publicaciones científicas pueden interesar a los usuarios basado en sus publicaciones científicas en Scopus y Orcid; y de servicio, en donde se permite a otras aplicaciones consumir las recomendaciones generadas por el sistema.Tesi

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

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available