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Journal articles on the topic 'Personalization'

To see the other types of publications on this topic, follow the link: Personalization.

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2024

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1

Kaneko, Kazuki, Yusuke Kishita, and Yasushi Umeda. "Conducting Personalization Design Workshops — Designing Personalization Procedures." Procedia CIRP 98 (2021): 494–99. http://dx.doi.org/10.1016/j.procir.2021.01.140.

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2

Foshee, Cecile M., and Brian C. Nelson. "Avatar Personalization." International Journal of Gaming and Computer-Mediated Simulations 6, no. 2 (April 2014): 1–14. http://dx.doi.org/10.4018/ijgcms.2014040101.

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The authors explored the influence of avatar personalization on students' competence beliefs in a virtual world-based assessment environment. The avatar personalization options allowed seventh and eighth grade students (n = 110) to customize the appearance of their avatar and provided them with the option to personalize their in-world conversations by choosing a name for their avatar. Based on self-determination theory the authors hypothesized that the personalization in a virtual-world environment would influence students' motivation to engage by stimulating their situational interest and enable positive competence beliefs and deeper engagement. The results indicated that personalization stimulated situational interest and situational interest significantly predicted competence beliefs. This research contributes to the body of literature on the personalization principle by extending this work to a virtual environment. The findings highlight the importance of addressing students' affective needs and illustrate the value of interest; which can potentially influence students' attitudes towards testing and towards science.

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3

Adomavicius, Gediminas, and Alexander Tuzhilin. "Personalization technologies." Communications of the ACM 48, no. 10 (October 2005): 83–90. http://dx.doi.org/10.1145/1089107.1089109.

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4

Kim, Chin-Woo. "Price Personalization." LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY 13, no. 2 (December 31, 2022): 43–81. http://dx.doi.org/10.34267/cbstl.2022.13.2.43.

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Product prices are now one of the many areas of our daily life in which artificial intelligence is becoming increasingly important. Digitization, especially online trading, opens up completely new possibilities and gives price adjustments a big boost in various ways. Thanks to their computing speed, algorithms can not only change and vary prices very quickly. Their enormous capacities for collecting and processing information even make it possible to include consumer data in the price calculation and thus adjust prices individually. Although we have become accustomed to uniform prices, the price has never been a purely static construct. Fluctuating prices are not a new phenomenon. Pricing freedom prevails within the framework of private autonomy. The aim of this paper is to examine how Korean law can respond to the new challenges posed by algorithmic price diversity. The law currently seems to offer little help against the currently existing lack of transparency in relation to the pricing process, which can go hand in hand with automated price individualization. So far, there are no legal regulations for disclosing how prices are determined. A central concern when dealing with individualized prices concerns the existence of information asymmetries and the (in)transparency of pricing strategies.

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5

Li, Cong. "When does web-based personalization really work? The distinction between actual personalization and perceived personalization." Computers in Human Behavior 54 (January 2016): 25–33. http://dx.doi.org/10.1016/j.chb.2015.07.049.

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6

Schreiber, Kristin L., and Jochen D. Muehlschlegel. "Personalization over Protocolization." Anesthesiology 134, no. 3 (January 19, 2021): 363–65. http://dx.doi.org/10.1097/aln.0000000000003695.

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7

Ono, Akinori. "Customization and Personalization." Japan Marketing Journal 40, no. 1 (June 30, 2020): 3–5. http://dx.doi.org/10.7222/marketing.2020.030.

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8

Rodrigues, Luiz, Paula T. Palomino, Armando M. Toda, Ana C. T. Klock, Wilk Oliveira, Anderson P. Avila-Santos, Isabela Gasparini, and Seiji Isotani. "Personalization Improves Gamification." Proceedings of the ACM on Human-Computer Interaction 5, CHI PLAY (October 5, 2021): 1–25. http://dx.doi.org/10.1145/3474714.

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Personalization of gamification is an alternative to overcome the shortcomings of the one-size-fits-all approach, but the few empirical studies analyzing its effects do not provide conclusive results. While many user and contextual information affect gamified experiences, prior personalized gamification research focused on a single user characteristic/dimension. Therefore, we hypothesize if a multidimensional approach for personalized gamification, considering multiple (user and contextual) information, can improve user motivation when compared to the traditional implementation of gamification. In this paper, we test that hypothesis through a mixed-methods sequential explanatory study. First, 26 participants completed two assessments using one of the two gamification designs and self-reported their motivations through the Situational Motivation Scale. Then, we conducted semi-structured interviews to understand learners' subjective experiences during these assessments. As result, the students using the personalized design were more motivated than those using the one-size-fits-all approach regarding intrinsic motivation and identified regulation. Furthermore, we found the personalized design featured game elements suitable to users' preferences, being perceived as motivating and need-supporting. Thus, informing i) practitioners on the use of a strategy for personalizing gamified educational systems that is likely to improve students' motivations, compared to OSFA gamification, and ii) researchers on the potential of multidimensional personalization to improve single-dimension strategies. For transparency, dataset and analysis procedures are available at https://osf.io/grzhp.

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9

Schneider, Hanna, Florian Lachner, Malin Eiband, Ceenu George, Purvish Shah, Chinmay Parab, Anjali Kukreja, Heinrich Hussmann, and Andreas Butz. "Privacy and personalization." Interactions 25, no. 3 (April 23, 2018): 52–55. http://dx.doi.org/10.1145/3197571.

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10

Volokh, Eugene. "Personalization and privacy." Communications of the ACM 43, no. 8 (August 2000): 84–88. http://dx.doi.org/10.1145/345124.345155.

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11

Keefe, James W. "What is Personalization?" Phi Delta Kappan 89, no. 3 (November 2007): 217–23. http://dx.doi.org/10.1177/003172170708900312.

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12

Riedl, J. "Personalization and privacy." IEEE Internet Computing 5, no. 6 (2001): 29–31. http://dx.doi.org/10.1109/4236.968828.

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13

Teevan, Jaime, Susan T. Dumais, and Eric Horvitz. "Potential for personalization." ACM Transactions on Computer-Human Interaction 17, no. 1 (March 2010): 1–31. http://dx.doi.org/10.1145/1721831.1721835.

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14

Burke, Victoria I., and Robin D. Burke. "Powerlessness and Personalization." International Journal of Applied Philosophy 33, no. 2 (2019): 319–43. http://dx.doi.org/10.5840/ijap202034131.

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Is privacy the key ethical issue of the internet age? This coauthored essay argues that even if all of a user’s privacy concerns were met through secure communication and computation, there are still ethical problems with personalized information systems. Our objective is to show how computer-mediated life generates what Ernesto Laclou and Chantal Mouffe call an “atypical form of social struggle.” Laclau and Mouffe develop a politics of contingent identity and transient articulation (or social integration) by means of the notions of absent, symbolic, hegemonic power and antagonistic transitions or relations. In this essay, we introduce a critical approach to one twenty-first-century atypical social struggle that, we claim, has a disproportionate effect on those who experience themselves as powerless. Our aim is to render explicit the forms of social mediation and distortion that result from large-scale machine learning as applied to personal preference information. We thus bracket privacy in order to defend some aspects of the EU GDPR that will give individuals more control over their experience of the internet if they want to use it and, thereby, decrease the unwanted epistemic effects of the internet. Our study is thus a micropolitics in in the Deleuzian micropolitical sense and a preliminary analysis of an atypical social struggle.

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15

Kobsa, Alfred. "Privacy-enhanced personalization." Communications of the ACM 50, no. 8 (August 2007): 24–33. http://dx.doi.org/10.1145/1278201.1278202.

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16

Garcia-Rivadulla, Sandra. "Personalization vs. privacy." IFLA Journal 42, no. 3 (September 27, 2016): 227–38. http://dx.doi.org/10.1177/0340035216662890.

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17

Iyengar, R., R. B. Altman, O. Troyanskya, and G. A. FitzGerald. "Personalization in practice." Science 350, no. 6258 (October 15, 2015): 282–83. http://dx.doi.org/10.1126/science.aad5204.

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18

Bender, W. "Community and personalization." IBM Systems Journal 35, no. 3.4 (1996): 367–68. http://dx.doi.org/10.1147/sj.353.0367.

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19

Kintzer, Eric. "Going beyond personalization." ACM SIGMOD Record 29, no. 2 (June 2000): 517. http://dx.doi.org/10.1145/335191.335459.

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20

Bernstein, Michael S., Desney Tan, Greg Smith, Mary Czerwinski, and Eric Horvitz. "Personalization via friendsourcing." ACM Transactions on Computer-Human Interaction 17, no. 2 (May 2010): 1–28. http://dx.doi.org/10.1145/1746259.1746260.

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21

DeWaters, Carrie. "Getting Personalization Right!" Reading Teacher 71, no. 2 (June 13, 2017): 221–24. http://dx.doi.org/10.1002/trtr.1612.

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22

KARIMI MANSOUB, Samira, Gönenç ERCAN, and İlyas ÇİÇEKLİ. "Selective personalization and group profiles for improved web search personalization." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 3 (May 8, 2020): 1631–43. http://dx.doi.org/10.3906/elk-1909-9.

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23

Eapen, Joel, and Adhithyan V S. "Personalization and Customization of LLM Responses." International Journal of Research Publication and Reviews 4, no. 12 (December 18, 2023): 2617–27. http://dx.doi.org/10.55248/gengpi.4.1223.123512.

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24

Kaneko, Kazuki, Yusuke Kishita, and Yasushi Umeda. "Proposal for the Design of Personalization Procedure." International Journal of Automation Technology 12, no. 6 (November 5, 2018): 833–41. http://dx.doi.org/10.20965/ijat.2018.p0833.

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The personalization of products and services is expected to become an indispensable feature of manufacturing. Because the concept of personalization ranges over a variety of domains from the manufacturing of a prosthetic foot to a web recommendation, there exist no commonly applicable design methodologies for product personalization. The purpose of this research is to propose methods of personalization. Therefore, this paper is focused on designing procedures of personalization, which include preparation, readout, design, production and provision, use, and feedback. For supporting the design of personalization procedure, we developed ten personalization strategies, a step-flow model, and a design procedure. Finally, the advantages of this method are investigated through a case study.

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25

Surprenant, Carol F., and Michael R. Solomon. "Predictability and Personalization in the Service Encounter." Journal of Marketing 51, no. 2 (April 1987): 86–96. http://dx.doi.org/10.1177/002224298705100207.

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Service marketers are confronted with two conflicting goals when designing service delivery systems, efficiency and personalization. The relative importance of each factor is determined by the nature of the specific service to be rendered, and by participants’ expectations about degree of personalization. A study was conducted to test two assertions: (1) service personalization is a multidimensional construct and (2) all forms of personalization do not necessarily result in greater consumer satisfaction with the service offering. Three types of personalization strategies were proposed and operationalized in a simulated banking setting. Evaluations of service encounters that differed in the degree and type of personalization employed indicate that personalization is not a unitary phenomenon and must be approached carefully in the context of service design.

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26

Moraru, Victor. "New media strategies: personalization of politics." Moldoscopie, no. 2(97) (February 2023): 137–44. http://dx.doi.org/10.52388/1812-2566.2022.2(97).13.

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During the recent years, there has been an increased interest paid for the personalization of politics. At the electoral competitions, modern democracies have witnessed to changes in establishing consensus, more focused on static ideological values, group affiliations and party-centred politics, rather on political candidates’ personality characteristics, their image, and voter’s personal choices. The new order in the field of politics has redefined pragmatism for political actors, who, in their political action, use many ways that can provide increased visibility and operate with the full potential of media arsenal. So, the general institutional personalization leads to personalization in the media, which in turn leads to personalization in the politicians’ behaviour. Respectively, the main sources of political information for society have imposed personalization as the general means of communication. In this context, the relationship between political power and the ability to influence media agendas is a key issue within the field of political communication examinations. That political personalization can be better understood by considering the personalization in the media. This article focuses on the personalization model in the media activity, examines diverse aspects of the concept of personalization, it presents the experience accumulated by the media in covering the political process through the comprehension of the personalization as test of notoriety.

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27

Woo, Mihye, and Kyounghee Ko. "Personalization as a Teaching and Learning Device to Enhance Communication Competence and Personalization in the Elementary English Curriculum and Textbook." Korea Association of Primary English Education 29, no. 1 (March 31, 2023): 79–109. http://dx.doi.org/10.25231/pee.2023.29.1.79.

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Acknowledging the value of personalization as an effective teaching and learning device to enhance competence in communication, this study aims to examine the 2015 Revised Primary English Curriculum and textbooks to investigate the contents and activities related to personalization. The study examined the curriculum related to speaking and writing for the contents that can be linked to personalization. It also examined three most popular textbook series of grades 3 through 6 to analyze the frequency, language function areas, the periods of a unit, and the types of personalization activities. The results of the study shows that personalization-related contents appear throughout the curriculum. However, more comprehensively and systematically written curriculum is desired in order to provide elementary learners a more personalization-rich learning environment. In terms of the frequencies, language function areas, and the periods of a unit of personalization activities, there was also room for improvement. Suggestions for writing a more comprehensive and systematic curriculum are provided and recommendations are made toward providing a learning environment that is more enriched in personalization activities, more balanced in speaking and writing, and more varied in the periods of units that personalization activities are utilized.

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28

Wu, Hongchen, Huaxiang Zhang, Lizhen Cui, and Xinjun Wang. "CEPTM: A Cross-Edge Model for Diverse Personalization Service and Topic Migration in MEC." Wireless Communications and Mobile Computing 2018 (August 12, 2018): 1–12. http://dx.doi.org/10.1155/2018/8056195.

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For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self-disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single-edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

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29

Perugini, Saverio, and Naren Ramakrishnan. "Personalization by Program Slicing." Journal of Object Technology 4, no. 3 (2005): 5. http://dx.doi.org/10.5381/jot.2005.4.3.a1.

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30

Potey, Madhuri A., and Pradeep K. Sinha. "Personalization Approaches for Ranking." International Journal of Information Retrieval Research 7, no. 1 (January 2017): 1–16. http://dx.doi.org/10.4018/ijirr.2017010101.

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Search engine technologies are evolving to satisfy the user's ever increasing information need; but are yet to achieve perfection especially in ranking. With the exponential growth in the available information on the internet; ranking has become vital for satisfactory search experience. User satisfaction can be ensured to some extent by personalizing the search results based on user preferences which can be explicitly stated or learned from user's search behavior. Machine learning algorithms which predict user preference from the available information related to the user are extensively experimented for personalization. Among several studies undertaken for re-ranking the documents, many focus on the user. Such approaches create user model to capture the search context and behavior. This study attempts to analyze the research trends in user model based personalization and discuss experimental results in personalized information retrieval area. The authors experimented to extend the state of the art in the specific areas of personalization.

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31

Chadwick, Ruth. "Selfies, Personalization and Bioethics." Bioethics 29, no. 3 (February 6, 2015): ii. http://dx.doi.org/10.1111/bioe.12159.

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32

Bennett, W. Lance. "The Personalization of Politics." ANNALS of the American Academy of Political and Social Science 644, no. 1 (October 3, 2012): 20–39. http://dx.doi.org/10.1177/0002716212451428.

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This article proposes a framework for understanding large-scale individualized collective action that is often coordinated through digital media technologies. Social fragmentation and the decline of group loyalties have given rise to an era of personalized politics in which individually expressive personal action frames displace collective action frames in many protest causes. This trend can be spotted in the rise of large-scale, rapidly forming political participation aimed at a variety of targets, ranging from parties and candidates, to corporations, brands, and transnational organizations. The group-based “identity politics” of the “new social movements” that arose after the 1960s still exist, but the recent period has seen more diverse mobilizations in which individuals are mobilized around personal lifestyle values to engage with multiple causes such as economic justice (fair trade, inequality, and development policies), environmental protection, and worker and human rights.

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33

Ball, Dwayne, Pedro S. Coelho, and Manuel J. Vilares. "Service personalization and loyalty." Journal of Services Marketing 20, no. 6 (October 2006): 391–403. http://dx.doi.org/10.1108/08876040610691284.

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34

Li, Tsai‐Yen, Sheng‐Luen Chung, and Kuo‐Kan Liao. "Personalization for network marketing." Journal of the Chinese Institute of Engineers 27, no. 5 (July 2004): 621–30. http://dx.doi.org/10.1080/02533839.2004.9670910.

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35

Powell, Jason L., and Tony Gilbert. "Personalization and sustainable care." Journal of Care Services Management 5, no. 2 (April 2011): 79–86. http://dx.doi.org/10.1179/175016811x12966389037718.

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36

Caprara, Gian Vittorio, Shalom H. Schwartz, Michele Vecchione, and Claudio Barbaranelli. "The Personalization of Politics." European Psychologist 13, no. 3 (January 2008): 157–72. http://dx.doi.org/10.1027/1016-9040.13.3.157.

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We examine how the traits and values of both candidates and voters contribute to political choice, using the five-factor model of traits and the Schwartz (1992 ) theory of basic personal values. 1,164 subjects reported their voting intention, their own traits and values, and those they perceived in the leader of one Italian political coalition (center-right or center-left). As hypothesized, voters simplified their personality judgments of politicians. Instead of the 5 trait factors and 10 basic values they employed in self-descriptions, they described the politicians using 2 trait factors (integrity and leadership) and 2 value dimensions (concern for others vs. self and excitement vs. caution). Logistic regressions revealed that voters own values predicted voting intention as hypothesized based on the policies advocated by the coalitions. Values trumped both own traits and demographic characteristics. The perceived traits and values of candidates accounted for additional variance in voting intention. The traits on which voters perceived a politician as weaker (Prodis leadership and Berlusconis integrity) were more decisive in orienting political preferences. We discuss explanations and implications of these findings.

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37

Bernhaupt, Regina, Michael M. Pirker, Astrid Weiss, David Wilfinger, and Manfred Tscheligi. "Security, privacy, and personalization." Computers in Entertainment 9, no. 3 (November 2011): 1–33. http://dx.doi.org/10.1145/2027456.2027463.

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38

Fink, Josef, Jürgen Koenemann, Stephan Noller, and Ingo Schwab. "Putting personalization into practice." Communications of the ACM 45, no. 5 (May 2002): 41–42. http://dx.doi.org/10.1145/506218.506242.

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39

Wang, Jun, Maarten Clements, Jie Yang, Arjen P. de Vries, and Marcel J. T. Reinders. "Personalization of tagging systems." Information Processing & Management 46, no. 1 (January 2010): 58–70. http://dx.doi.org/10.1016/j.ipm.2009.06.002.

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40

Berry, Christopher, Hui Wang, and S. Jack Hu. "Product architecting for personalization." Journal of Manufacturing Systems 32, no. 3 (July 2013): 404–11. http://dx.doi.org/10.1016/j.jmsy.2013.04.012.

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41

Ho, Shuk Ying, Michael J. Davern, and Kar Yan Tam. "Personalization and choice behavior." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 39, no. 4 (October 31, 2008): 31–47. http://dx.doi.org/10.1145/1453794.1453800.

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42

Stell, Roxanne, and Nita L. Paden. "Building Relationships Through Personalization." Journal of Customer Service in Marketing & Management 4, no. 2 (March 25, 1998): 53–64. http://dx.doi.org/10.1300/j127v04n02_04.

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43

Churchill, Elizabeth F. "Putting thepersonback into personalization." interactions 20, no. 5 (September 1, 2013): 12. http://dx.doi.org/10.1145/2512050.2504847.

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44

Tseng, M. M., R. J. Jiao, and C. Wang. "Design for mass personalization." CIRP Annals 59, no. 1 (2010): 175–78. http://dx.doi.org/10.1016/j.cirp.2010.03.097.

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45

Zimmermann, Andreas, Marcus Specht, and Andreas Lorenz. "Personalization and Context Management." User Modeling and User-Adapted Interaction 15, no. 3-4 (August 2005): 275–302. http://dx.doi.org/10.1007/s11257-005-1092-2.

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46

Gwizdka, Jacek, Nicholas Belkin, Susan Dumais, Luanne Freund, and Susan Gauch. "Multiple facets of personalization." Proceedings of the American Society for Information Science and Technology 46, no. 1 (2009): 1–7. http://dx.doi.org/10.1002/meet.2009.145046017.

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47

Karwatzki, Sabrina, Olga Dytynko, Manuel Trenz, and Daniel Veit. "Beyond the Personalization–Privacy Paradox: Privacy Valuation, Transparency Features, and Service Personalization." Journal of Management Information Systems 34, no. 2 (April 3, 2017): 369–400. http://dx.doi.org/10.1080/07421222.2017.1334467.

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48

Kaneko, Kazuki, Yusuke Kishita, and Yasushi Umeda. "Toward Developing a Design Method of Personalization: Proposal of a Personalization Procedure." Procedia CIRP 69 (2018): 740–45. http://dx.doi.org/10.1016/j.procir.2017.11.134.

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49

Lambillotte, Laetitia, Yakov Bart, and Ingrid Poncin. "When Does Information Transparency Reduce Downside of Personalization? Role of Need for Cognition and Perceived Control." Journal of Interactive Marketing 57, no. 3 (July 4, 2022): 393–420. http://dx.doi.org/10.1177/10949968221095557.

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Companies increasingly use personalization to offer a better experience to their customers. Online personalization enables them to learn from customers’ data and adapt their website content accordingly. Although customers may value personalization, it may also trigger privacy concerns. In this context, both regulators and firms need a better understanding of the process underlying the effect of personalization on privacy concerns, as well as the role of information transparency in this process. Drawing on signaling theory, the authors propose how perceived control may mediate the negative impact of personalization on privacy concerns and hypothesize that the interaction effect of personalization and information transparency depends on customer need for cognition. Findings from two experimental studies show that perceived control is lower on personalized websites than on nonpersonalized websites, which leads to privacy concerns. However, the presence of a transparency message can mitigate the negative effect of website personalization for customers who are in low need for cognition.

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50

Tarmizi and Lidiana. "Enhancing User Experience in E-commerce through Personalization Algorithms A Study on Information System Design." Journal Informatic, Education and Management (JIEM) 6, no. 1 (February 17, 2024): 24–28. http://dx.doi.org/10.61992/jiem.v6i1.59.

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This research aims to improve user experience in e-commerce through the use of personalization algorithms in information system design. The research methods used involve literature analysis, prototype development, and user testing. Literature analysis was conducted to understand the concept of personalization, relevant algorithms, and factors influencing user experience in e-commerce. Based on this understanding, a prototype e-commerce information system with personalization features was implemented. User testing is carried out to collect data about user experiences before and after implementing a personalization algorithm. The research results show that the use of personalization algorithms significantly improves user experience in e-commerce. Users report feeling more engaged, increased relevance of content, and ease in finding products that match their preferences. Apart from that, this research also identifies several important factors that need to be considered in the design of e-commerce information systems that use personalization algorithms, such as data privacy, transparency, and user control. In conclusion, the use of personalization algorithms can effectively improve user experience in e-commerce, with the important note of considering relevant factors.

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