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 -