Layered evaluation of multi-criteria collaborative filtering for scientific paper recommendation

Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. Keywords: Recommender systems; Multi-Criteria Decision Making (MCDM); Evaluatio

A Survey of Greek Agricultural E-Markets

The role that information technology plays in today’s business activities has led to an increase in firms using and/or deploying e-markets online. This development undoubtedly affects the agri-food sector, since a large number of agricultural firms are demonstrating or are expected to demonstrate e-commerce activities. This paper aims to provide an overview of the current status of agricultural e-markets in Greece, by presenting results from an analysis of 100 cases. Results indicate that Greek e-markets may still have a rather low degree of sophistication, but they demonstrate a strong B2B orientation, as well as an outreach for international customer bases.Internet, e-commerce, e-markets, agriculture, agri-food sector, survey, Consumer/Household Economics, Marketing,

Towards a Social Trust-Aware Recommender for Teachers

Fazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2014). Towards a Social Trust-aware Recommender for Teachers. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 177-194): Springer New York.Online communities and networked learning provide teachers with social learning opportunities, allowing them to interact and collaborate with others in order to develop their personal and professional skills. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this . The setting is the Open Discovery Space (ODS) project. Unfortunately, due to the sparsity of the educational datasets most educational recommender systems cannot make accurate recommendations. To overcome this problem, we propose to enhance a trust-based recommender algorithm with social data obtained from monitoring the activities of teachers within the ODS platform. In this article, we outline the re-quirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical ev-idence from a survey study with stakeholders of the ODS project to support the requirements identified from a literature study. Finally, we present an agenda for further research intended to find out which recommender system should ul-timately be deployed in the ODS platform.NELLL, EU 7th framework Open Discovery Spac

A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.LinkedU

Applying the Nominal Group Technique for Metadata Training of Domain Experts

Low metadata quality is a problem faced by most digital repositories, affecting resource discoverability and the overall quality of services and search mechanisms that are supported by these repositories. Metadata training of human annotators presents itself as a major challenge to contribute towards higher metadata quality for the digital resources hosted in repositories. This paper discusses the positive results of previous approaches to metadata training in the cases of a educational, cultural and scientific/research repositories, and it attempts to improve them by using the Nominal Group technique. © Springer International Publishing Switzerland 2013

Layered evaluation in recommender systems: A retrospective assessment

Evaluation of recommender systems has only lately started to become more systematic, since the emphasis has long been on the experimental evaluation of algorithmic performance. Recent studies have proposed adopting a layered evaluation approach, according to which recommender systems may be decomposed into several components, evaluating each of them separately. Nevertheless, there are still no evaluation studies of recommender systems that apply a layered evaluation framework to explore how all the different components or layers of such a system may be assessed. This paper introduces layered evaluation and examines how a previously proposed layered evaluation framework for adaptive systems can be applied in the case of recommender systems. It presents the possible adaptation of this layered framework that may fit the interaction components of recommender systems. Then, it focuses on a specific recommender system and carries out a retrospective analysis of its past evaluation results under the new prism that the layered evaluation approach brings. Our analysis indicates that implementing a layeredbased evaluation of recommender systems has the potential to facilitate a more detailed and informed evaluation of such systems, allowing researchers and developers to better understand how to improve them