14,305 research outputs found
The Impacts of Privacy Rules on Users' Perception on Internet of Things (IoT) Applications: Focusing on Smart Home Security Service
Department of Management EngineeringAs communication and information technologies advance, the Internet of Things (IoT) has changed the way people live. In particular, as smart home security services have been widely commercialized, it is necessary to examine consumer perception. However, there is little research that explains the general perception of IoT and smart home services. This article will utilize communication privacy management theory and privacy calculus theory to investigate how options to protect privacy affect how users perceive benefits and costs and how those perceptions affect individuals??? intentions to use of smart home service. Scenario-based experiments were conducted, and perceived benefits and costs were treated as formative second-order constructs. The results of PLS analysis in the study showed that smart home options to protect privacy decreased perceived benefits and increased perceived costs. In addition, the perceived benefits and perceived costs significantly affected the intention to use smart home security services. This research contributes to the field of IoT and smart home research and gives practitioners notable guidelines.ope
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
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
A Utility-Theoretic Approach to Privacy in Online Services
Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users
The Role of the Mangement Sciences in Research on Personalization
We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,
Adaptive Municipal e-forms
Adaptation of electronic forms seems to be a step forward to reduce the burden for people who fill in forms. Municipalities more and more offer eforms online that can be used to request a municipal product or service. To create adaptive e-forms that satisfy the need of end-users, involvement of those users in design activities and evaluation is necessary. This paper describes the design of adaptive municipal e-forms and the way user-groups were involved in the design activities and will be involved in evaluation
Towards a semantic modeling of learners for social networks
The Friend of a Friend (FOAF) ontology is a vocabulary for mapping social networks. In this paper we propose an extension to FOAF in order to allow it to model learners and their social networks. We analyse FOAF alongside different learner modeling standards and specifications, and based on this analysis we introduce a taxonomy of the different features found in those models. We then compare the learner models and FOAF against the taxonomy to see how their characteristics have been shaped by their purpose. Based on this we propose extensions to FOAF in order to produce a learner model that is capable of forming the basis of a semantic social network.<br/
- …