The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Perfil de la investigación académica sobre Juegos Masivos en Línea para Múltiples Jugadores (JMLMJ) 2000-2009: Horizontes para la investigación educativa

DOI: http://dx.doi.org/10.17227/01234870.38folios75.94Whilst there exists a large body of publications around Massively Multiplayer On-line Role-Play Gaming (MMORPG), there is little profiling academic research on this type of game. This study aims at unveiling what, when, where and who constitute scholarly work in research about MMORPG. A 777-register dataset was configured with primary documents taken from 16 databases and two web-portals. The dataset was drilled down using specialized text-mining software. Findings revealed four main research interests that comprise the games themselves, gaming experiences, systems architecture and educational MMORPG. It was also found that research on this topic started out in 2002 and some milestones of emerging research were charted out. The most prolific organizations and authors were also identified in which the USA, Canada and Italy occupy outstanding places. It is recommended that research profiling studies be carried out to extendmore informed literature reviews and support further research questions.La investigación sobre Juegos Masivos en Línea para Múltiples Jugadores (JMLMJ) es amplia; sin embargo, no hay mucha literatura especializada que perfile la investigación sobre este tipo específico de juegos. El presente estudio persigue describir el qué, el cuándo, el dónde y el quién que constituyen trabajo investigativo y académico sobre los JMLMJ. Se configuró una base de datos de 777 registros con documentos de investigación provenientes de 16 bases de datos académicas y dos portales web. La base de datos que se organizó fue explorada utilizando un software especializado en minería de textos. Los resultados revelan cuatro tendencias principales en la investigación sobre los JMLMJ: los juegos en sí mismos, las experiencias de juego, los sistemas de arquitectura de estos juegos y los JMLMJ relacionados con el fenómeno educativo. Se encontró que la investigación sobre estos juegos se origina en 2002 y se encontraron rutas de investigación relacionadas como desarrollo del campo. Se identificaron los autores más prolíficos quienes son provenientes de organizaciones en USA, Canadá e Italia. Se recomienda la realización de estudios de perfil para ampliar las revisiones de literatura que sustente la formulación de preguntas de investigación

Evolution of Decision Support Systems Research Field in Numbers

The scientific production in a certain field shows, in great extent, the research interests in that field. Decision Support Systems are a particular class of information systems which are gaining more popularity in various domains. In order to identify the evolution in time of the publications number, authors, subjects, publications in the Decision Support Systems (DSS) field, and therefore the scientific world interest for this field, in November 2010 there have been organized a series of queries on three major international scientific databases: ScienceDirect, IEEE Xplore Digital Library and ACM Digital Library. The results presented in this paper shows that, even the decision support systems research field started in 1960s, the interests for this type of systems grew exponentially with each year in the last decades.DSS, Numbers, Research, Materials

New Talent Signals: Shiny New Objects or a Brave New World?

Almost 20 years after McKinsey introduced the idea of a war for talent, technology is disrupting the talent identification industry. From smartphone profiling apps to workplace big data, the digital revolution has produced a wide range of new tools for making quick and cheap inferences about human potential and predicting future work performance. However, academic industrial–organizational (I-O) psychologists appear to be mostly spectators. Indeed, there is little scientific research on innovative assessment methods, leaving human resources (HR) practitioners with no credible evidence to evaluate the utility of such tools. To this end, this article provides an overview of new talent identification tools, using traditional workplace assessment methods as the organizing framework for classifying and evaluating new tools, which are largely technologically enhanced versions of traditional methods. We highlight some opportunities and challenges for I-O psychology practitioners interested in exploring and improving these innovations

Reviews

Brian Clegg, Mining The Internet — Information Gathering and Research on the Net, Kogan Page: London, 1999. ISBN: 0–7494–3025–7. Paperback, 147 pages, £9.99

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future