Transparent user models for personalization. El-Arini, K., Paquet, U., Herbrich, R., Van Gael, J., & Agüera y Arcas, B. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 678--686, 2012. ACM.
Transparent user models for personalization [link]Paper  abstract   bibtex   
Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it. In this work, we propose a framework for addressing this problem by developing a new user-interpretable feature set upon which to base personalized recommendations. These features, which we call badges, represent fundamental traits of users (e.g., “vegetarian” or “Apple fanboy”) inferred by modeling the interplay between a user’s behavior and self-reported identity. Specifically, we consider the microblogging site Twitter, where users provide short descriptions of themselves in their profiles, as well as perform actions such as tweeting and retweeting. Our approach is based on the insight that we can define badges using high precision, low recall rules (e.g., “Twitter profile contains the phrase ‘Apple fanboy”’), and with enough data, generalize to other users by observing shared behavior. We develop a fully Bayesian, generative model that describes this interaction, while allowing us to avoid the pitfalls associated with having positive-only data. Experiments on real Twitter data demonstrate the effectiveness of our model at capturing rich and interpretable user traits that can be used to provide transparency for personalization.
@inproceedings{el-arini_transparent_2012,
	title = {Transparent user models for personalization},
	url = {http://dl.acm.org/citation.cfm?id=2339639},
	abstract = {Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it.
In this work, we propose a framework for addressing this problem by developing a new user-interpretable feature set upon which to base personalized recommendations. These features, which we call badges, represent fundamental traits of users (e.g., “vegetarian” or “Apple fanboy”) inferred by modeling the interplay between a user’s behavior and self-reported identity. Specifically, we consider the microblogging site Twitter, where users provide short descriptions of themselves in their profiles, as well as perform actions such as tweeting and retweeting. Our approach is based on the insight that we can define badges using high precision, low recall rules (e.g., “Twitter profile contains the phrase ‘Apple fanboy”’), and with enough data, generalize to other users by observing shared behavior. We develop a fully Bayesian, generative model that describes this interaction, while allowing us to avoid the pitfalls associated with having positive-only data.
Experiments on real Twitter data demonstrate the effectiveness of our model at capturing rich and interpretable user traits that can be used to provide transparency for personalization.},
	urldate = {2017-01-10},
	booktitle = {Proceedings of the 18th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining},
	publisher = {ACM},
	author = {El-Arini, Khalid and Paquet, Ulrich and Herbrich, Ralf and Van Gael, Jurgen and Agüera y Arcas, Blaise},
	year = {2012},
	keywords = {transparency, interpretability},
	pages = {678--686},
	annote = {Author Keywords
Personalization, Twitter, graphical models, transparency
The article sets up the personalization of online content as problematic due to the fact that 1) users are unaware of it 2) users are not aware of how the personalization system "perceives them" and have no recourse to correct this perception and 3) users may not want personalized content or such personalization may have negative consequences. The authors set out to make "personalization more transparent". They do so by probing the use of "ID badges" that correspond with user affiliations, and then comparing self-reported badges to predicted badges from Twitter ocntent.},
	file = {ElArini-TransparentUserModelsforPersonalization.pdf:C\:\\Users\\Ashudeep Singh\\Zotero\\storage\\HUR2D2VZ\\ElArini-TransparentUserModelsforPersonalization.pdf:application/pdf}
}
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