Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen

INSPIRAL: investigating portals for information resources and learning. Final project report

INSPIRAL's aims were to identify and analyse, from the perspective of the UK HE learner, the nontechnical, institutional and end-user issues with regard to linking VLEs and digital libraries, and to make recommendations for JISC strategic planning and investment. INSPIRAL's objectives -To identify key stakeholders with regard to the linkage of VLEs, MLEs and digital libraries -To identify key stakeholder forum points and dissemination routes -To identify the relevant issues, according to the stakeholders and to previous research, pertaining to the interaction (both possible and potential) between VLEs/MLEs and digital libraries -To critically analyse identified issues, based on stakeholder experience and practice; output of previous and current projects; and prior and current research -To report back to JISC and to the stakeholder communities, with results situated firmly within the context of JISC's strategic aims and objectives

Web Service Discovery Based on Past User Experience

Web service technology provides a way for simplifying interoperability among different organizations. A piece of functionality available as a web service can be involved in a new business process. Given the steadily growing number of available web services, it is hard for developers to find services appropriate for their needs. The main research efforts in this area are oriented on developing a mechanism for semantic web service description and matching. In this paper, we present an alternative approach for supporting users in web service discovery. Our system implements the implicit culture approach for recommending web services to developers based on the history of decisions made by other developers with similar needs. We explain the main ideas underlying our approach and report on experimental results

Early evaluation of Unistats: user experiences

This paper sets out the findings of the user evaluation of Unistats.UK Higher Education Funding Bodie

Hikester - the event management application

Today social networks and services are one of the most important part of our everyday life. Most of the daily activities, such as communicating with friends, reading news or dating is usually done using social networks. However, there are activities for which social networks do not yet provide adequate support. This paper focuses on event management and introduces "Hikester". The main objective of this service is to provide users with the possibility to create any event they desire and to invite other users. "Hikester" supports the creation and management of events like attendance of football matches, quest rooms, shared train rides or visit of museums in foreign countries. Here we discuss the project architecture as well as the detailed implementation of the system components: the recommender system, the spam recognition service and the parameters optimizer