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

Mobile recommender systems:Identifying the major concepts

© The Author(s) 2018. This article identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalised recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the Internet and networking infrastructure have brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain

Privacy-preserving recommendations in context-aware mobile environments

© Emerald Publishing Limited. Purpose - This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use aconsiderable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach - This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protectionin mind, which isdone byusing realistic dummy parameter creation. Todemonstrate the applicability of the method, arelevant context-aware data set has been used to run performance and usability tests. Findings - The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value - This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used

Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems

Typically, recommender systems from any domain, be it movies, music, restaurants, etc., are organized in a centralized fashion. The service provider holds all the data, biases in the recommender algorithms are not transparent to the user, and the service providers often create lock-in effects making it inconvenient for the user to switch providers. In this paper, we argue that the user's smartphone already holds a lot of the data that feeds into typical recommender systems for movies, music, or POIs. With the ubiquity of the smartphone and other users in proximity in public places or public transportation, data can be exchanged directly between users in a device-to-device manner. This way, each smartphone can build its own database and calculate its own recommendations. One of the benefits of such a system is that it is not restricted to recommendations for just one user - ad-hoc group recommendations are also possible. While the infrastructure for such a platform already exists - the smartphones already in the palms of the users - there are challenges both with respect to the mobile recommender system platform as well as to its recommender algorithms. In this paper, we present a mobile architecture for the described system - consisting of data collection, data exchange, and recommender system - and highlight its challenges and opportunities.Comment: Accepted for publication at the 2019 IEEE 16th International Conference on Ubiquitous Intelligence and Computing (IEEE UIC 2019

Factors Influencing the Quality of the User Experience in Ubiquitous Recommender Systems

The use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using Ubiquitous recommender systems. However in mobile devices there are different factors that need to be considered in order to get more useful recommendations and increase the quality of the user experience. This paper gives an overview of the factors related to the quality and proposes a new hybrid recommendation model.Comment: The final publication is available at www.springerlink.com Distributed, Ambient, and Pervasive Interactions Lecture Notes in Computer Science Volume 8530, 2014, pp 369-37

Tourism Mobile Recommender Systems: A Survey

​The growth in tourism industry shows a positive trend in Indonesia because of human needs, infrastructure, and the Internet to support it. To link these aspects, tourists need mobile recommender systems to make everything handle and control easily. From one system tourists can get access to the information, plan their itinerary, get the suggestion, and share the experience. The collaborative, content and hybrid method become the source to enjoy the journey. This paper will show the progress in a last ten years, and what future research that still open to explore

Design Criteria for Transparent Mobile Event Recommendations

Recommender systems assist the user to overcome the information overflow of today’s information society. When a recommendation failed, user’s trust in a system decreases due to the fact that most recommender systems act as black boxes. They don’t offer any insight into the systems logic and cannot be questioned as it is normal for recommendations between humans. Users don’t know how and which personal information is processed. Transparency, which is about explaining to the user why a recommendation is made, allows understanding the nature of a recommendation. Within a mobile environment, it is possible to address the user more individualized but transparency needs a completely different way of visualization and interaction. The paper in hand aims at an analysis of a survey which asked about the kind of style element as well as how much information should be visualized on a mobile device in order to offer transparency

Generating context-aware recommendations using banking data in a mobile recommender system

The increasing adoption of smartphones by the society has created a new area of research in recommender systems. This new domain is based on using location and context-awareness to provide personalization. This paper describes a model to generate context-aware recommendations for mobile recommender systems using banking data in order to recommend places where the bank customers have previously spent their money. In this work we have used real data provided by a well know Spanish bank. The mobile prototype deployed in the bank Labs environment was evaluated in a survey among 100 users with good results regarding usefulness and effectiveness. The results also showed that test users had a high confidence in a recommender system based on real banking data