Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia

Only few digital libraries and reference managers offer recommender systems, although such systems could assist users facing information overload. In this paper, we introduce Mr. DLib's recommendations-as-a-service, which allows third parties to easily integrate a recommender system into their products. We explain the recommender approaches implemented in Mr. DLib (content-based filtering among others), and present details on 57 million recommendations, which Mr. DLib delivered to its partner GESIS Sowiport. Finally, we outline our plans for future development, including integration into JabRef, establishing a living lab, and providing personalized recommendations.Comment: Accepted for publication at the JCDL conference 201

Improving cold-start recommendations using item-based stereotypes

Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model

Intelligent, Item-Based Stereotype Recommender System

Recommender systems (RS) have become key components driving the success of e-commerce, and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity, the vastness of the data, and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RS, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. This work propose a set of methodologies for the automatic generation of stereotypes during the cold-starts. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. Recommender Systems using the primitive metadata features (baseline systems) as well as factorisation-based systems are used as benchmarks for state-of-the-art methodologies to assess the results of the proposed approach under a wide range of recommendation quality metrics. The results demonstrate how such generic groupings of the metadata features, when performed in a manner that is unaware and independent of the user’s community preferences, may greatly reduce the dimension of the recommendation model, and provide a framework that improves the quality of recommendations in the cold start

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