@inproceedings{fdccafdbac6945ebbcd63b02aa187b45,
title = "Putting users in control of their recommendations",
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.",
keywords = "Collaborative filtering, MovieLens, Personalization, Recommender systems, Simulation study, Social computing, User control, User study",
author = "Max Harper and Funing Xu and Harmanpreet Kaur and Kyle Condiff and Shuo Chang and Loren Terveen",
year = "2015",
month = sep,
day = "16",
doi = "10.1145/2792838.2800179",
language = "English (US)",
series = "RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "3--10",
booktitle = "RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems",
note = "9th ACM Conference on Recommender Systems, RecSys 2015 ; Conference date: 16-09-2015 Through 20-09-2015",
}