@inproceedings{harper_putting_2015,
	address = {New York, NY, USA},
	title = {Putting {Users} in {Control} of {Their} {Recommendations}},
	url = {http://doi.acm.org/10.1145/2792838.2800179},
	doi = {10.1145/2792838.2800179},
	abstract = {The essence of a recommender system is that it can recommend items
personalized to the preferences of an individual user. But typically users
are given no explicit control over this personalization, and are instead
left guessing about how their actions affect the resulting
recommendations. We hypothesize that any recommender algorithm will better
fit some users' expectations than others, leaving opportunities for
improvement. To address this challenge, we study a recommender that puts
some control in the hands of users. Specifically, we build and evaluate a
system that incorporates user-tuned popularity and recency modifiers,
allowing users to express concepts like "show more popular items". We find
that users who are given these controls evaluate the resulting
recommendations much more positively. Further, we find that users diverge
in their preferred settings, confirming the importance of giving control
to users.},
	urldate = {2015-09-19},
	booktitle = {{RecSys} '15},
	publisher = {ACM},
	author = {Harper, F Maxwell and Xu, Funing and Kaur, Harmanpreet and Condiff, Kyle and Chang, Shuo and Terveen, Loren},
	year = {2015},
	note = {Journal Abbreviation: RecSys '15},
	pages = {3--10},
}

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