Social Relations and Methods in Recommender Systems: A Systematic Review

With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations

Sistemas de recomendación en educación: una reseña de los mecanismos de recomendación en entornos de aprendizaje virtual (E-learning)

In recent years, new trends and methodologies have emerged that greatly favor the education sector. E-learning as an alternative to regular teaching and learning processes has transformed the educational dynamics thanks to the inclusion of MOOCs, personal learning environments, allowing the educational process to be carried out at a personalized level where the focus is on learning styles and the profile of the student. This article presents a review of current works around machine learning mechanisms to make recommendations in the educational environment, where it is found that besides the discovery of the student’s learning style, it is important to know their level of knowledge and learning speed, in addition to the tools used by the student to carry out their studies. Finally, the opportunity for implementation and research of these issues in Colombia is highlighted. Recientemente, han emergido nuevas tendencias y metodologías que han favorecido enormemente al sector educativo. El e-learning como alternativa a los procesos de enseñanza y aprendizaje regulares ha transformado las dinámicas educativas debido a la inclusión de los MOOC, entornos personales de aprendizaje, permitiendo que el proceso educativo sea llevado a cabo en un nivel personalizado en donde el foco esté puesto en los estilos de aprendizaje y el perfil del estudiante. Este artículo presenta una revisión de trabajos actuales alrededor de mecanismos de aprendizaje de máquina para hacer recomendaciones en el entorno educativo, en donde se encuentra que, aparte del descubrimiento del estilo de aprendizaje del estudiante, es importante conocer su nivel de conocimiento y su velocidad de aprendizaje, así como las herramientas usadas por el estudiante para llevar a cabo sus estudios. Finalmente, se hace énfasis en la oportunidad de implementar y seguir investigando estas cuestiones en Colombia.&nbsp

Decoding learning: the proof, promise and potential of digital education

With hundreds of millions of pounds spent on digital technology for education every year – from interactive whiteboards to the rise of one–to–one tablet computers – every new technology seems to offer unlimited promise to learning. many sectors have benefitted immensely from harnessing innovative uses of technology. cloud computing, mobile communications and internet applications have changed the way manufacturing, finance, business services, the media and retailers operate. But key questions remain in education: has the range of technologies helped improve learners’ experiences and the standards they achieve? or is this investment just languishing as kit in the cupboard? and what more can decision makers, schools, teachers, parents and the technology industry do to ensure the full potential of innovative technology is exploited? There is no doubt that digital technologies have had a profound impact upon the management of learning. institutions can now recruit, register, monitor, and report on students with a new economy, efficiency, and (sometimes) creativity. yet, evidence of digital technologies producing real transformation in learning and teaching remains elusive. The education sector has invested heavily in digital technology; but this investment has not yet resulted in the radical improvements to learning experiences and educational attainment. in 2011, the Review of Education Capital found that maintained schools spent £487 million on icT equipment and services in 2009-2010. 1 since then, the education system has entered a state of flux with changes to the curriculum, shifts in funding, and increasing school autonomy. While ring-fenced funding for icT equipment and services has since ceased, a survey of 1,317 schools in July 2012 by the british educational suppliers association found they were assigning an increasing amount of their budget to technology. With greater freedom and enthusiasm towards technology in education, schools and teachers have become more discerning and are beginning to demand more evidence to justify their spending and strategies. This is both a challenge and an opportunity as it puts schools in greater charge of their spending and use of technolog

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability

[...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...

TIC para la educación: sistema adaptativo basado en mecanismos de aprendizaje automático para la apropiación de tecnologías en estudiantes de educación media

The research presents an approach based on data analytics and automatic learning mechanisms integrated into one of the most widely used digital learning platforms in the world (open EDX) as a contribution to the improvement of learning processes in middle school students in municipalities of Colombia. Methodologically, according to Creswell y Plano Clark (2007), and Clements et al. (2017) a system was created to recommend educational contents adapted to the individual characteristics of students considering the limitations in the use of technologies in educational institutions; we developed interviews with teachers and focus groups with students of 10º and 11º grade; as a result, a functional architecture proposal was generated that allows the generation of initial recommendations of contents managed according to the students' performance and the characteristics of the territory.La investigación presenta un enfoque basado en analítica de datos y mecanismos de aprendizaje automático integrado a una de las plataformas digitales de aprendizaje más usadas en el mundo (open EDX) como aporte al mejoramiento de los procesos de aprendizaje en estudiantes de educación media de municipios de Colombia. Metodológicamente con base en Creswell y Plano Clark (2007) y Clements et al. (2017), se construyó un sistema que posibilita la recomendación de contenidos educativos adecuados a las características individuales de estudiantes teniendo en cuenta las limitaciones en el uso y apropiación social de las tecnologías en las instituciones educativas, se realizaron entrevistas con docentes y grupos focales con estudiantes de grados 10 y 11 de media vocacional; como resultado se generó una propuesta de arquitectura funcional que permite la generación de recomendaciones iniciales de contenidos administrados según el desempeño de los estudiantes y las características propias del territorio

A recommendations-based reading list system prototype for learning and resource management

A reading list is a list of reading items recommended by an academic to assist students' acquisition of knowledge for a specific subject. Subsequently, the libraries of higher education institutions collect and assemble reading lists according to specific courses and offer the students the reading lists service. However, the reading list is created based on localised intelligence, restricted to the academic’s knowledge of their field, semantics, experience and awareness of developments. This investigation aims to present the views and comments of academics, and library staff, on an envisaged aggregated reading lists service, which aggregates recommended reading items from various higher education institutions. This being the aim, we build a prototype, which aggregates reading lists from different universities and showcase it to nineteen academics and library staff in various higher education institutions to capture their views, comments and any recommendations. In the process we also showcase the feasibility of collecting and aggregating reading lists, in addition to understanding the process of reading lists creation at their respective higher education institutions. The prototype successfully showcases the creation of ranked lists of reading items, authors, topics, modules and courses. Academics and library staff indicated that aggregated lists would collectively benefit the academic community. Consequently, recommendations in the form of process implementations and technological applications are made to overcome and successfully implement the proposed aggregated reading list service. This proof-of-concept demonstrates potential benefits for the academic community and identifies further challenges to overcome in order to scale it up to the implementation stage

Design and Evaluation of Techniques to Utilize Implicit Rating Data in Complex Information Systems.

Research in personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users - where it is possible to classify items involved and to make personalization based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as Digital Libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. In this report, we present techniques for collecting, storing, processing, and utilizing implicit rating data of Digital Libraries for analysis and decision support. We present our pilot study to find virtual user groups using implicit rating data. We demonstrate the effectiveness of implicit rating data for characterizing users and finding virtual user communities, through statistical hypothesis testing. Further, we describe a visual data mining tool named VUDM (Visual User model Data Mining tool) that utilizes implicit rating data. We provide the results of formative evaluation of VUDM and discuss the problems raised and plans for further studies

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