Collaborative Filtering with Preferences Inferred from Brain Signals
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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.
Originalsprog | Engelsk |
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Titel | The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021 |
Udgivelsessted | New York |
Forlag | Association for Computing Machinery, Inc. |
Publikationsdato | 2021 |
Sider | 602-611 |
ISBN (Elektronisk) | 9781450383127 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenien Varighed: 19 apr. 2021 → 23 apr. 2021 |
Konference
Konference | 2021 World Wide Web Conference, WWW 2021 |
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Land | Slovenien |
By | Ljubljana |
Periode | 19/04/2021 → 23/04/2021 |
Sponsor | Amazon, et al., Facebook, FINVOLUTION, Microsoft Research, Pinterest |
Bibliografisk note
Funding Information:
The research was partially funded by the Academy of Finland (Decision numbers: 322653, 328875, 336085). Computing resources were provided by the Finnish Grid and Cloud Infrastructure (persistent id: urn:nbn:fi:research-infras-2016072533).
Publisher Copyright:
© 2021 ACM.
ID: 306898594