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

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A user profile for information filtering using RFID-SIM card in pervasive network

The appearance of new technologies allows new data processing techniques. Thus, many new data processing techniques make difficult to user to find pertinent information in suitable time, unless knowing what accurately is in search of, where and how getting it. This paper proposes a pervasive network based information filtering system that integrates user profile such as identity, preference and other important data. User profile is embarked in a RFID-SIM card in order to guarantee its privacy, flexibility, mobility and confidentiality. The overall system objectives are privacy, security and providing pertinent information to the user according to his profile at anytime, anywhere, and in any form. The design and implementation of the system is also presented

Leveraging Smart Technology for User Experience Personalization – A Comparative Case Study of Innovative Payment Systems

Background: This study seeks to understand how the attributes of smart technology (SMT) can be leveraged to enable personalized services and optimize unique user experiences to attract and retain customers. Based on Kang et al.’s (2020) study of SMT attributes and quality effects and Liang et al.’s (2006) study on personalized recommendation and user satisfaction, we constructed a SMT personalization model to analyze how the SMT attributes of smart functionality and smart content enable personalization in different ways and create unique customer experiences throughout the user journey. Method: Two representative payment systems were selected to depict how they integrated the strengths of personalized smart functionalities and contents to innovate their business models, optimize user experiences, and sustain business growth. Results: Based on the comparative analysis of the two payment cases, the functionality and content attributes of smart chips and omni-channel platforms were explored, and the tailored advisory and responsive support for customers both offline and online were validated. Conclusion: The life-enriching service innovations provide valuable insights for leveraging SMT for personalization. It is hoped that the SMT personalization model can be extended to other types of SMT applications and can be used as a framework for designing innovative services

Minds Online: The Interface between Web Science, Cognitive Science, and the Philosophy of Mind

Alongside existing research into the social, political and economic impacts of the Web, there is a need to study the Web from a cognitive and epistemic perspective. This is particularly so as new and emerging technologies alter the nature of our interactive engagements with the Web, transforming the extent to which our thoughts and actions are shaped by the online environment. Situated and ecological approaches to cognition are relevant to understanding the cognitive significance of the Web because of the emphasis they place on forces and factors that reside at the level of agent–world interactions. In particular, by adopting a situated or ecological approach to cognition, we are able to assess the significance of the Web from the perspective of research into embodied, extended, embedded, social and collective cognition. The results of this analysis help to reshape the interdisciplinary configuration of Web Science, expanding its theoretical and empirical remit to include the disciplines of both cognitive science and the philosophy of mind

THOR: A Hybrid Recommender System for the Personalized Travel Experience

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected

Towards a Smarter organization for a Self-servicing Society

Traditional social organizations such as those for the management of healthcare are the result of designs that matched well with an operational context considerably different from the one we are experiencing today. The new context reveals all the fragility of our societies. In this paper, a platform is introduced by combining social-oriented communities and complex-event processing concepts: SELFSERV. Its aim is to complement the "old recipes" with smarter forms of social organization based on the self-service paradigm and by exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track on Emergent Technologies for Ambient Assisted Living (ETAAL