CASPER: Mining Personalized Services

Author(s):  
Jeongkyu Park ◽  
Keung Hae Lee
2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sheenam Jain ◽  
Malin Sundström

Purpose Today, customers’ perceived value does not only depend on the products, but also on the services provided by a firm. In e-commerce, it is important to shift the focus beyond the product and discuss the value of personalized services in the context of e-commerce fulfillment. Therefore, the purpose of this paper is twofold: to develop a conceptual framework proposing satisfaction through personalized services as a middle-range theory; and to suggest foundational premises supporting the theoretical framework, which in turn shape middle-range theory within the context of apparel e-commerce fulfillment. Design/methodology/approach In this theory-driven paper, the authors apply the scientific circle of enquiry, as it demonstrates the role of theorizing with the help of middle-range theory and empirical evidence and as such provides a methodological scaffolding that connects theory formulation and verification. The authors synthesize literature related to customer perceived value (CPV) and satisfaction, followed by abduction focusing on understanding the empirical domain as it occurred in practice from company cases. The presented case studies are based on semi-structured interviews with three Swedish online retailers within the apparel industry. The theory-driven analysis results in suggestions of foundational premises. Findings Based on the theoretical foundations and empirical generalizations, three propositions are suggested. The premises regarding satisfaction through personalized service applied in the domain of apparel e-commerce fulfillment are: to ensure customer satisfaction requires a value co-creation perspective using data during the pre-purchase phase; to ensure customer satisfaction and retention require added-value perspective during the post-purchase phase of the shopping journey; and to ensure satisfaction and convenience require an added-value perspective at the last mile. Practical implications The apparel firms lose a substantial amount of revenue because of poor online customer satisfaction, leading to e-commerce not reaching its full potential. To enhance customer value, online retailers need to find a resort in advanced technologies and analytics to address customer satisfaction, and it is suggested that retailers shift their focus beyond the products and find ways to improve personalized service offerings to gain market advantage, improve fulfillment, drive sales and increase CPV. Originality/value To consider personalized services as a source for improving e-commerce fulfillment and CPV, the main contribution of this study is conceptual as it presents a theoretical model developed from general theory, middle-range theory and verified with empirical claims.


2005 ◽  
Vol 277-279 ◽  
pp. 287-292 ◽  
Author(s):  
Lu Na Byon ◽  
Jeong Hye Han

As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.


2020 ◽  
Author(s):  
Luis Eduardo Costa Laurindo ◽  
Ivan Rodrigues de Moura ◽  
Carleandro De Oliveira Nolêto ◽  
Flavio Sergio da Silva ◽  
André Luiz Almeida Cardoso

Currently, tour guides can be implemented through mobile technologiessuch as smartphones and wearable devices. The penetrationof these technologies into people’s daily lives has made it possibleto implement more sophisticated and personalized services, revolutionizingthe tourism industry. However, the process of developingsuch applications is complex and involves the knowledge of variousexperts, such as programmers and designers. So this article devisedan authoring tool titled inTourMobile, which allows non-expertpeople to develop their mobile tour guides easily and intuitively.The usability of the designed tool was evaluated, in which it wasobserved its efficiency to assist in the development of mobile tourguides.


2010 ◽  
Vol 10 (2) ◽  
pp. 111-119 ◽  
Author(s):  
Jong-Hun Kim ◽  
Jee-Song Park ◽  
Eun-Young Jung ◽  
Dong-Kyun Park ◽  
Young-Ho Lee

Author(s):  
Anastasia Kozyreva ◽  
Philipp Lorenz-Spreen ◽  
Ralph Hertwig ◽  
Stephan Lewandowsky ◽  
Stefan M. Herzog

AbstractPeople rely on data-driven AI technologies nearly every time they go online, whether they are shopping, scrolling through news feeds, or looking for entertainment. Yet despite their ubiquity, personalization algorithms and the associated large-scale collection of personal data have largely escaped public scrutiny. Policy makers who wish to introduce regulations that respect people’s attitudes towards privacy and algorithmic personalization on the Internet would greatly benefit from knowing how people perceive personalization and personal data collection. To contribute to an empirical foundation for this knowledge, we surveyed public attitudes towards key aspects of algorithmic personalization and people’s data privacy concerns and behavior using representative online samples in Germany (N = 1065), Great Britain (N = 1092), and the United States (N = 1059). Our findings show that people object to the collection and use of sensitive personal information and to the personalization of political campaigning and, in Germany and Great Britain, to the personalization of news sources. Encouragingly, attitudes are independent of political preferences: People across the political spectrum share the same concerns about their data privacy and show similar levels of acceptance regarding personalized digital services and the use of private data for personalization. We also found an acceptability gap: People are more accepting of personalized services than of the collection of personal data and information required for these services. A large majority of respondents rated, on average, personalized services as more acceptable than the collection of personal information or data. The acceptability gap can be observed at both the aggregate and the individual level. Across countries, between 64% and 75% of respondents showed an acceptability gap. Our findings suggest a need for transparent algorithmic personalization that minimizes use of personal data, respects people’s preferences on personalization, is easy to adjust, and does not extend to political advertising.


Author(s):  
Bai Liming ◽  
Alex Ishiwata Gavino ◽  
Pius Lee ◽  
Kim Jungyoon ◽  
Liu Na ◽  
...  

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