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Personality Capture and EmulationRecommender Systems

Personality Capture and Emulation: Recommender Systems [Integration of two existing research traditions, one in computer science and the other in social science, could become the basis for a new convergent discipline of potentially revolutionary significance, cultural science. Recommender systems are a well-developed part of online commerce, targeting advertising to specific customers on the basis of the individual’s probable preferences, but they have not yet seen much use across the social sciences. At the same time, quantitative research methodologies for studying culture, such as preference questions in sociological questionnaires and anthropological databases like the Human Relations Area Files are generally ignored outside very narrow academic communities. Recommender systems are already well-prepared to emulate an individual’s preferences, but only within the narrow range of the particular commercial products covered. Pilot studies using the Netflix database show that it is possible to categorize movies in culturally-relevant terms. Other studies using data from questionnaires administered to college students show how preferences for academic subjects can be connected to gender and political orientation, and factor analyzed to reveal academic subcultures. Food preferences and preferences for fiction reading, plus studies of preference concordance across friendships and within ethnic groups, illustrate a range of other directions that a new, rigorous cultural science could explore.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Personality Capture and EmulationRecommender Systems

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Publisher
Springer London
Copyright
© Springer-Verlag London 2014
ISBN
978-1-4471-5603-1
Pages
75 –99
DOI
10.1007/978-1-4471-5604-8_4
Publisher site
See Chapter on Publisher Site

Abstract

[Integration of two existing research traditions, one in computer science and the other in social science, could become the basis for a new convergent discipline of potentially revolutionary significance, cultural science. Recommender systems are a well-developed part of online commerce, targeting advertising to specific customers on the basis of the individual’s probable preferences, but they have not yet seen much use across the social sciences. At the same time, quantitative research methodologies for studying culture, such as preference questions in sociological questionnaires and anthropological databases like the Human Relations Area Files are generally ignored outside very narrow academic communities. Recommender systems are already well-prepared to emulate an individual’s preferences, but only within the narrow range of the particular commercial products covered. Pilot studies using the Netflix database show that it is possible to categorize movies in culturally-relevant terms. Other studies using data from questionnaires administered to college students show how preferences for academic subjects can be connected to gender and political orientation, and factor analyzed to reveal academic subcultures. Food preferences and preferences for fiction reading, plus studies of preference concordance across friendships and within ethnic groups, illustrate a range of other directions that a new, rigorous cultural science could explore.]

Published: Nov 13, 2013

Keywords: Recommender System; Science Fiction; Preference Rating; Preference Scale; Academic Subject

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