Collaborative Filtering with Preferences Inferred from Brain Signals
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Collaborative Filtering with Preferences Inferred from Brain Signals. / Davis, Keith M.; SpapA©, Michiel; Ruotsalo, Tuukka.
The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021. New York : Association for Computing Machinery, Inc., 2021. p. 602-611.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Collaborative Filtering with Preferences Inferred from Brain Signals
AU - Davis, Keith M.
AU - SpapA©, Michiel
AU - Ruotsalo, Tuukka
N1 - Publisher Copyright: © 2021 ACM.
PY - 2021
Y1 - 2021
N2 - Collaborative filtering is a common technique in which interaction data from a large number of users are used to recommend items to an individual that the individual may prefer but has not interacted with. Previous approaches have achieved this using a variety of behavioral signals, from dwell time and clickthrough rates to self-reported ratings. However, such signals are mere estimations of the real underlying preferences of the users. Here, we use brain-computer interfacing to infer preferences directly from the human brain. We then utilize these preferences in a collaborative filtering setting and report results from an experiment where brain inferred preferences are used in a neural collaborative filtering framework. Our results demonstrate, for the first time, that brain-computer interfacing can provide a viable alternative for behavioral and self-reported preferences in realistic recommendation scenarios. We also discuss the broader implications of our findings for personalization systems and user privacy.
AB - Collaborative filtering is a common technique in which interaction data from a large number of users are used to recommend items to an individual that the individual may prefer but has not interacted with. Previous approaches have achieved this using a variety of behavioral signals, from dwell time and clickthrough rates to self-reported ratings. However, such signals are mere estimations of the real underlying preferences of the users. Here, we use brain-computer interfacing to infer preferences directly from the human brain. We then utilize these preferences in a collaborative filtering setting and report results from an experiment where brain inferred preferences are used in a neural collaborative filtering framework. Our results demonstrate, for the first time, that brain-computer interfacing can provide a viable alternative for behavioral and self-reported preferences in realistic recommendation scenarios. We also discuss the broader implications of our findings for personalization systems and user privacy.
KW - Brain signals
KW - Brain-computer interface
KW - Collaborative filtering
KW - Eeg
U2 - 10.1145/3442381.3450031
DO - 10.1145/3442381.3450031
M3 - Article in proceedings
AN - SCOPUS:85108015550
SP - 602
EP - 611
BT - The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PB - Association for Computing Machinery, Inc.
CY - New York
T2 - 2021 World Wide Web Conference, WWW 2021
Y2 - 19 April 2021 through 23 April 2021
ER -
ID: 306898594