The Personalization Willingness Paradox: An Empirical Evaluation of Sharing Information and Prospective Benefit of Online Consumers

Online enterprises today use information about customers to improve service and design personalized offerings. To do this successfully, however, enterprises must collect consumer information. This study enhances awareness about a central paradox for firms investing in personalization; namely, that consumers who value information utility are also more likely to participate in personalization. We examine the relationship between prospective benefit and consumer willingness to share information for online personalization. Based on a survey of over 800 online consumers, we examine the question of whether customer perceived information valuable is associated with consumer willingness to be profiled online. Our results indicate that customers who desire greater profits will have a greater level of trust, and then more willing to be profiled. This result poses a dilemma for firms, is the bought information accurate and reliable? In order to manage this dilemma, we suggest that enterprises build trust for the core values and knowledge management systems that address the needs of consumers, and adopt a strategy of providing personalization features accepting that the privacy sensitive minority of consumers

Personalisation, Participation and Care

Personalisation services are developing in England as a social policy response to user demands for more tailored, effective and flexible forms of health and social care support. Across England and Wales, this process is being implemented under the personalization which is also seen as a vehicle for promoting service user rights through increasing participation, empowerment and control while also promoting self-restraint by having users manage the costs of their health and social care. This paper reviews the existing research evidence for personalization, albeit limited, and identifies themes for future research

APPS 2021: Third International Workshop on Adaptive and Personalized Privacy and Security

The work has been partially supported by the EU Horizon 2020 Grant 826278 “Securing Medical Data in Smart Patient-Centric Healthcare Systems” (Serums), and by a new European project, TRUSTID - Intelligent and Continuous Online Student Identity Management for Improving Security and Trust in European Higher Education Institutions, which is funded by the European Commission within the Erasmus+ 2020 Programme.The Third International Workshop on Adaptive and Personalized Privacy and Security (APPS 2021) aims to bring together researchers and practitioners working on diverse topics related to understanding and improving the usability of privacy and security software and systems, by applying user modeling, adaptation and personalization principles. Our special focus in 2021 is on challenges and opportunities related to the Covid-19 outbreak, more specifically on ensuring security and privacy of sensitive data and secure user interactions in online systems. The third edition of the workshop includes interdisciplinary contributions from Belgium, Cyprus, Germany, Greece, Portugal, the Netherlands, and United Kingdom, that introduce new and disruptive ideas, suggest novel solutions, and present research results about various aspects (theory, applications, tools) for bringing user modeling, adaptation and personalization principles into privacy and systems security. This summary gives a brief overview of APPS 2021, held online in conjunction with the 29th ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2021).Postprin

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201