期刊
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
卷 -, 期 -, 页码 602-611出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3442381.3450031
关键词
Brain-computer interface; collaborative filtering; brain signals; eeg
类别
资金
- Academy of Finland [322653, 328875, 336085]
- Academy of Finland (AKA) [328875, 328875, 322653, 336085, 322653] Funding Source: Academy of Finland (AKA)
By using brain-computer interfacing to infer preferences directly from the human brain, a new possibility for collaborative filtering recommendation systems is introduced. Through experimentation, it was demonstrated that brain-computer interfacing can serve as a viable alternative for behavioral and self-reported preferences, with broad implications for practical applications.
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.
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