Towards a killer app for the Semantic Web

Killer apps are highly transformative technologies that create new markets and widespread patterns of behaviour. IT generally, and the Web in particular, has benefited from killer apps to create new networks of users and increase its value. The Semantic Web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. There are certain features that distinguish killer apps from other ordinary applications. This paper examines those features in the context of the Semantic Web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing Semantic Web applications

Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

Features for Killer Apps from a Semantic Web Perspective

There are certain features that that distinguish killer apps from other ordinary applications. This chapter examines those features in the context of the semantic web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing semantic web applications. Killer apps are highly tranformative technologies that create new e-commerce venues and widespread patterns of behaviour. Information technology, generally, and the Web, in particular, have benefited from killer apps to create new networks of users and increase its value. The semantic web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. The authors hope that this chapter will help to highlight some of the common ingredients of killer apps in e-commerce, and discuss how such applications might emerge in the semantic web

InnoJam: A Web 2.0 discussion platform featuring a recommender system

In this Master Thesis we have designed, implemented and evaluated a Web 2.0 platform for massive online-discussion, inspired by Innovation Jams. Innovation Jams, the original initiative from IBM, has proven to be successful at bringing together vast amounts of people, capturing their untapped knowledge and, while the participants are discussing, gather useful insights for a companyʼs innovation strategy [Spangler et al. 2006, Bjelland and Chapman Wood 2008]. Our approach, based in an open-source forum system, features visualization techniques and a recommender system in order to provide the participants in the Jam with useful insights and interesting discussion recommendations for an improved participation. A theoretical introduction and a state-of-the-art survey in recommender systems has been gathered in order to frame and support the design of the hybrid recommender system [Burke 2002], composed by a content-based and a collaborative filtering recommenders, developed for InnoJam

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Computer-Supported Collaborative Production

This paper proposes the concept of collaborative production as a focus of concern within the general area of collaborative work. We position the concept with respect to McGrath's framework for small group dynamics and the more familiar collaboration processes of awareness, coordination, and communication (McGrath 1991). After reviewing research issues and computer-based support for these interacting aspects of collaboration, we turn to a discussion of implications for how to design improved support for collaborative production. We illustrate both the challenges of collaborative production and our design implications with a collaborative map-updating scenario drawn from the work domain of geographical information systems