Nikos Manouselis

Analysis and Classification of Multi-Criteria Recommender Systems

Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems. The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of online users.

Metadata for digital collections of e‐government resources

Purpose – The purpose of this paper is to introduce a process for developing a metadata element set that will describe e‐government resources in digital collections. The outcome of the process is a metadata schema that reuses as many elements as possible from existing specifications and standards (termed as an e‐government metadata application profile). The use of e‐government metadata is to facilitate the electronic categorization and storage of governmental resources, as well as to enhance users electronic interactions with the public sector.Design/methodology/approach – The paper extends an initial process presented in the context of the European Standardization Committee CEN/ISSS, proposing four steps for developing the application profile: determine the resources to be described by the metadata, identify the stakeholder groups who will use the metadata, determine the use of metadata for each stakeholder group, and specify the metadata elements corresponding to each use.Findings – The steps of the pr...

Quality in metadata : a schema for e-commerce

Purpose – An issue of increased interest in metadata research concerns finding ways to store, in the metadata of an information resource, data regarding the resources quality. The purpose of this paper is to present a metadata schema that facilitates representation and storage of data related to the quality of an e‐commerce resource, the e‐commerce evaluation metadata (ECEM) schema.Design/methodology/approach – A study of quality approaches that can be applied for the evaluation of e‐commerce resources is provided. The ECEM schema structure and elements are described. To demonstrate how ECEM can be used, two indicative examples are given: describing an e‐commerce quality approach, and storing quality evaluation results. A discussion about the validation and implementation of ECEM is also provided.Findings – It has been demonstrated that ECEM can be effectively used to describe e‐commerce quality approaches. ECEM also facilitates the structured representation and storage of quality evaluation results. It ...

Comparing Different Metadata Application Profiles for Agricultural Learning Repositories

Agricultural learning repositories can provide new opportunities for sharing, accessing, using and reusing learning resources online. Metadata plays a crucial role in such systems: apart from simply indexing resources, metadata makes it easier to discover a learning resource in a repository, as well as to decide about ways to use it for teaching or learning purposes. In the context of agricultural education and training, a variety of appropriate metadata standards may be selected, adapted and implemented for a learning repository. In this paper we introduce the concept of metadata for agricultural learning resources, and compare two particular cases: one application profile based on the Dublin Core Metadata Element Set (DCMES) and the other based on the IEEE Learning Object Metadata (LOM). The paper attempts to identify similarities and differences between the two case studies and to outline issues that have to be resolved in order to harmonize such efforts.

M-government services for rural SMEs

The scope of this paper is twofold: firstly, to propose a framework that serves the public agencies in identifying potential m-government services to rural SMEs as well as a set of technical solutions for the provision of them; secondly, to present a web-based observatory that can help rural SMEs in finding accurate and updated information for the provided m-government services and being trained at anytime in using them. Also, security issues have been considered regarding the provided services, so that government agencies and users can trust them and feel confident in using them.

Survey and Analysis of TEL Recommender Systems

In this chapter, we present a framework for the analysis of existing recommender systems. Then, we present a detailed analysis of relevant TEL recommender systems along the dimensions defined by our framework.

TEL as a Recommendation Context

In this chapter, we define the TEL recommendation problem and identify TEL recommendation goals. More specifically, we reflect on user tasks that are supported in TEL settings, and how they compare to typical user tasks in other recommender systems. Then, we present an analysis of existing data sets that capture contextual learner interactions with tools and resources in TEL settings. These data sets can be used for a wide variety of research purposes, including experimental comparison of the performance of recommendation algorithms for learning.

Preliminary Study of the Expected Performance of MAUT Collaborative Filtering Algorithms

This paper presents the results of an initial study on how a set of multi-attribute utility collaborative filtering algorithms are expected to perform, under various experimental conditions. An online simulator has been used to produce a large number of synthetic data sets with varying properties. Then, the examined algorithms have been executed and evaluated upon all data sets, using different performance metrics. A statistical analysis of the results has followed, trying to make initial conclusions about the expected performance of the studied algorithms under different operational conditions.

A metadata model for e-markets

The objective of this paper is to present a specialised metadata model that has been developed in order to store e-market characteristics in a systematic, interoperable and reusable manner. The proposed model is based on the Dublin Core (DC) metadata standard, and termed as the DC E-Market (DC-EM) metadata model. The paper describes the DC-EM metadata model elements, providing guidelines for their encoding in the eXtensible Markup Language (XML). It also presents the prototype of a DC-EM metadata repository, and its application for the analysis of a sample of Greek agricultural e-markets.

Designing an Internet-based directory service for e-markets

The application of Internet technologies to online transactions has lead to the amazing growth of Internet-based electronic markets (e-markets). This paper describes an Internet-based directory service for e-markets that aims to facilitate online buyers by making the process of finding an e-market easier and less time-consuming. This service, termed e-Market Metadata (eMaM) directory service, is based on a metadata repository of e-markets descriptions. These descriptions store a number of e-market characteristics according to the Dublin Core for E-Markets (DC-EM) metadata model which has been developed for this purpose. In particular, this paper introduces metadata models and describes the DC-EM metadata model. Then, it presents the specification and implementation of the eMaM service using the Unified Modelling Language (UML). Finally, a pilot usage of the implemented eMaM service that examines 120 B2B, B2C, and C2C e-markets for various business sectors is discussed.