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Books on the topic 'Personalization of information'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Grant, Emanuel, Dimitris Kotzinos, Dominique Laurent, Nicolas Spyratos, and Yuzuru Tanaka, eds. Information Search, Integration, and Personalization. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43862-7.

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Kotzinos, Dimitris, Dominique Laurent, Nicolas Spyratos, Yuzuru Tanaka, and Rin-ichiro Taniguchi, eds. Information Search, Integration, and Personalization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30284-9.

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3

Kotzinos, Dimitrios, Yeow Wei Choong, Nicolas Spyratos, and Yuzuru Tanaka, eds. Information Search, Integration and Personalization. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38901-1.

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4

Kawtrakul, Asanee, Dominique Laurent, Nicolas Spyratos, and Yuzuru Tanaka, eds. Information Search, Integration, and Personalization. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08732-0.

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5

Flouris, Giorgos, Dominique Laurent, Dimitris Plexousakis, Nicolas Spyratos, and Yuzuru Tanaka, eds. Information Search, Integration, and Personalization. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44900-1.

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6

Tanaka, Yuzuru, Nicolas Spyratos, Tetsuya Yoshida, and Carlo Meghini, eds. Information Search, Integration and Personalization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40140-4.

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7

Heikkinen, Kari. Conceptualization of user-centric personalization management. Lappeenranta: Lappeenranta University of Technology, 2005.

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8

Bamshad, Mobasher, Desmarais Michel C, Nkambou Roger 1966-, and SpringerLink (Online service), eds. User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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9

Carberry, Sandra. User Modeling, Adaptation, and Personalization: 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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10

International Workshop on Semantic Media Adaption and Personalization (1st 2006 Athens, Greece). SMAP 2006: Proceedings : First International Workshop on Semantic Media Adaptation and Personalization : 4-5 December, 2006, Athens, Greece. Los Alamitos, Calif: IEEE Computer Society, 2006.

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11

International, Workshop on Semantic Media Adaptation and Personalization (3rd 2008 Prague Czech Republic). Proceedings: Third International Workshop on Semantic Media Adaptation and Personalization : December 15-16, 2008, Prague, Czech Republic. Los Alamitos, Calif: IEEE Computer Society, 2008.

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12

International, Workshop on Semantic Media Adaptation and Personalization (3rd 2008 Prague Czech Republic). Proceedings: Third International Workshop on Semantic Media Adaptation and Personalization : December 15-16, 2008, Prague, Czech Republic. Los Alamitos, Calif: IEEE Computer Society, 2008.

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13

Konstan, Joseph A. User Modeling, Adaption and Personalization: 19th International Conference, UMAP 2011, Girona, Spain, July 11-15, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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14

Spain) International Workshop on Semantic Media Adaptation and Personalization (4th 2009 San Sebastián. 2009 Fourth International Workshop on Semantic Media Adaptation and Personalization: Proceedings, 14-15 December 2009, San Sebastián, Spain. Los Alamitos, Calif: IEEE Computer Society, 2009.

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15

International Workshop on Semantic Media Adaption and Personalization (2nd 2007 London, England). SMAP 2007: Proceedings : Second International Workshop on Semantic Media Adaptation and Personalization : London, United Kingdom, 17-18 December, 2007. Los Alamitos, Calif: IEEE Computer Society, 2007.

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16

International Workshop on Semantic Media Adaption and Personalization (2nd 2007 London, England). SMAP 2007: Proceedings : Second International Workshop on Semantic Media Adaptation and Personalization : London, United Kingdom, 17-18 December, 2007. Los Alamitos, Calif: IEEE Computer Society, 2007.

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17

International Workshop on Semantic Media Adaption and Personalization (2nd 2007 London, England). SMAP 2007: Proceedings : Second International Workshop on Semantic Media Adaptation and Personalization : London, United Kingdom, 17-18 December, 2007. Los Alamitos, Calif: IEEE Computer Society, 2007.

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18

International Workshop on Semantic Media Adaption and Personalization (2nd 2007 London, England). SMAP 2007: Proceedings : Second International Workshop on Semantic Media Adaptation and Personalization : London, United Kingdom, 17-18 December, 2007. Los Alamitos, Calif: IEEE Computer Society, 2007.

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19

International Workshop on Semantic Media Adaption and Personalization (2nd 2007 London, England). SMAP 2007: Proceedings : Second International Workshop on Semantic Media Adaptation and Personalization : London, United Kingdom, 17-18 December, 2007. Los Alamitos, Calif: IEEE Computer Society, 2007.

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20

International Workshop on Semantic Media Adaption and Personalization (2nd 2007 London, England). SMAP 2007: Proceedings : Second International Workshop on Semantic Media Adaptation and Personalization : London, United Kingdom, 17-18 December, 2007. Los Alamitos, Calif: IEEE Computer Society, 2007.

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21

UMAP 2010 (2010 Hawaii Island, Hawaii). User modeling, adaptation, and personalization: 18th international conference, UMAP 2010, Big Islang, HI, USA, June 20-24, 2010 : proceedings. Berlin: Springer, 2010.

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22

Massimo, Zancanaro, Pianesi Fabio, McCalla Gord, and SpringerLink (Online service), eds. User Modeling, Adaptation, and Personalization: 17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy, June 22-26, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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23

Evers, Jodi J. A. The impact of mood intensity on informational style: Level of mood intensity on emotion content, focus on feelings, informativeness, personalization, and generalization. Sudbury, Ont: Laurentian University, Department of Psychology, 1999.

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24

G, Uchyigit, and Ma M. Y, eds. Personalization techniques and recommender systems. Hackensack, NJ: World Scientific, 2008.

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25

Information Search Integration and Personalization Communications in Computer and Information Science. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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26

Manolis, Wallace, Angelides Marios C, and Mylonas Phivos, eds. Advances in semantic media adaptation and personalization. Berlin: Springer Verlag, 2008.

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27

Wallace, Manolis, Marios C. Angelides, and Phivos Mylonas. Advances in Semantic Media Adaptation and Personalization. Springer, 2010.

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28

Manolis, Wallace, Angelides Marios C, and Mylonas Phivos, eds. Advances in semantic media adaptation and personalization. Berlin: Springer Verlag, 2008.

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29

The Customer Centric Enterprise: Advances in Mass Customization and Personalization. Springer, 2003.

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30

Bucy, Erik P., and Patrick Stewart. The Personalization of Campaigns: Nonverbal Cues in Presidential Debates. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.52.

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Nonverbal cues are important elements of persuasive communication whose influence in political debates are receiving renewed attention. Recent advances in political debate research have been driven by biologically grounded explanations of behavior that draw on evolutionary theory and view televised debates as contests for social dominance. The application of biobehavioral coding to televised presidential debates opens new vistas for investigating this time-honored campaign tradition by introducing a systematic and readily replicated analytical framework for documenting the unspoken signals that are a continuous feature of competitive candidate encounters. As research utilizing biobehavioral measures of presidential debates and other political communication progresses, studies are becoming increasingly characterized by the use of multiple methodologies and merging of disparate data into combined systems of coding that support predictive modeling.Key elements of nonverbal persuasion include candidate appearance, communication style and behavior, as well as gender dynamics that regulate candidate interactions. Together, the use of facial expressions, voice tone, and bodily gestures form uniquely identifiable display repertoires that candidates perform within televised debate settings. Also at play are social and political norms that govern candidate encounters. From an evaluative standpoint, the visual equivalent of a verbal gaffe is the commission of a nonverbal expectancy violation, which draws viewer attention and interferes with information intake. Through second screens, viewers are able to register their reactions to candidate behavior in real time, and merging biobehavioral and social media approaches to debate effects is showing how such activity can be used as an outcome measure to assess the efficacy of candidate nonverbal communication during televised presidential debates.Methodological approaches employed to investigate nonverbal cues in presidential debates have expanded well beyond the time-honored technique of content analysis to include lab experiments, focus groups, continuous response measurement, eye tracking, vocalic analysis, biobehavioral coding, and use of the Facial Action Coding System to document the muscle movements that comprise leader expressions. Given the tradeoffs and myriad considerations involved in analyzing nonverbal cues, critical issues in measurement and methodology must be addressed when conducting research in this evolving area. With automated coding of nonverbal behavior just around the corner, future research should be designed to take advantage of the growing number of methodological advances in this rapidly evolving area of political communication research.

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31

Information Search, Integration, and Personalization: International Workshop, ISIP 2013, Bangkok, Thailand, September 16--18, 2013. Revised Selected ... in Computer and Information Science). Springer, 2014.

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32

User Modeling Adaptation And Personalization 20th International Conference Umap 2012 Montreal Canada July 1620 2012 Proceedings. Springer, 2012.

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33

Tanaka, Yuzuru, Dimitris Plexousakis, Dominique Laurent, Nicolas Spyratos, and Giorgos Flouris. Information Search, Integration, and Personalization: 13th International Workshop, ISIP 2019, Heraklion, Greece, May 9–10, 2019, Revised Selected ... in Computer and Information Science ). Springer, 2020.

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34

Tanaka, Yuzuru, Nicolas Spyratos, Dimitrios Kotzinos, and Yeow Wei Choong. Information Search, Integration and Personalization: 9th International Workshop, ISIP 2014, Kuala Lumpur, Malaysia, October 9-10, 2014, Revised ... in Computer and Information Science). Springer, 2016.

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35

Tanaka, Yuzuru, Dimitris Kotzinos, Dominique Laurent, Nicolas Spyratos, and Rin-ichiro Taniguchi. Information Search, Integration, and Personalization: 12th International Workshop, ISIP 2018, Fukuoka, Japan, May 14–15, 2018, Revised Selected Papers ... in Computer and Information Science). Springer, 2019.

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36

Tanaka, Yuzuru, Emanuel Grant, Dimitris Kotzinos, Dominique Laurent, and Nicolas Spyratos. Information Search, Integration, and Personalization: 10th International Workshop, ISIP 2015, Grand Forks, ND, USA, October 1-2, 2015, Revised ... in Computer and Information Science). Springer, 2016.

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37

Kant, Tanya. Making it Personal. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190905088.001.0001.

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The encounter of “personalized experiences”—targeted advertisements, tailored information feeds, and “recommended” content, among other things—is now a common and somewhat inescapable component of digital life. More often than not however, “you” the user are not primarily responsible for personalizing your web engagements: instead, with the help of your search, browsing, and purchase histories, your “likes,” your click-throughs, and a multitude of other data you produce as you go about your day, your experience can “conveniently”—and computationally—be personalized on your behalf. This book explores a host of new questions that emerge from web users’ encounters with these forms of algorithmic personalization. What do users “know” about the algorithms that apparently “know” them? If personalization practices seek to act on users’ behalf (for instance, by deciding what content is personally relevant), then how do users retain or relinquish their autonomy? Indeed, what kinds of selfhoods are made possible when personalization algorithms intervene in identity construction? Making It Personal is the first full-length monograph to critically analyze the sociocultural implications of algorithmic personalization through the accounts and testimonies of web users themselves. At the heart of the book are interviews and focus groups with web users who—through a myriad of resistant, tactical, resigned, or trusting engagements—encounter algorithmic personalization as part of their lived experience on the web. The book proposes that for those who encounter it, algorithmic personalization creates new implications for knowledge production, autonomy, cultural capital, and formations of self.

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38

(Editor), Peter Brusilovsky, Alfred Kobsa (Editor), and Wolfgang Nejdl (Editor), eds. The Adaptive Web: Methods and Strategies of Web Personalization (Lecture Notes in Computer Science). Springer, 2007.

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39

User Modeling, Adaptation and Personalization: 22nd International Conference, UMAP 2014, Aalborg, Denmark, July 7-11, 2014. Proceedings. Springer, 2014.

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40

Draper, Nora A., and Joseph Turow. Audience Constructions, Reputations, and Emerging Media Technologies. Edited by Roger Brownsword, Eloise Scotford, and Karen Yeung. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199680832.013.68.

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This chapter traces how changes in media and surveillance technologies have influenced the strategies producers have for constructing audiences. The largely unregulated practices of information gathering that inform the measurement and evaluation of audiences have consequences for how individuals are viewed by media producers and, consequently, for how they view themselves. Recent technological advances have increased the specificity with which advertisers target audiences—moving from the classification of audience groups based on shared characteristics to the personalization of commercial media content for individuals. To assist in the personalization of content, media producers and advertisers use interactive technologies to enlist individuals in the construction of their own consumer reputations. Industry discourse frames the resulting personalization as empowering for individuals who are given a hand in crafting their media universe; however, these strategies are more likely to create further disparity among those who media institutions do and do not view as valuable.

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41

1947-, Tseng Mitchell M., and Piller Frank T. 1969-, eds. The customer centric enterprise: Advances in mass customization and personalizaton. Berlin: Springer, 2003.

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