  In recent years, short video platforms have gained widespread popularity,
making the quality of video recommendations crucial for retaining users.
Existing recommendation systems primarily rely on behavioral data, which faces
limitations when inferring user preferences due to issues such as data sparsity
and noise from accidental interactions or personal habits. To address these
challenges and provide a more comprehensive understanding of user affective
experience and cognitive activity, we propose EEG-SVRec, the first EEG dataset
with User Multidimensional Affective Engagement Labels in Short Video
Recommendation. The study involves 30 participants and collects 3,657
interactions, offering a rich dataset that can be used for a deeper exploration
of user preference and cognitive activity. By incorporating selfassessment
techniques and real-time, low-cost EEG signals, we offer a more detailed
understanding user affective experiences (valence, arousal, immersion,
interest, visual and auditory) and the cognitive mechanisms behind their
behavior. We establish benchmarks for rating prediction by the recommendation
algorithm, showing significant improvement with the inclusion of EEG signals.
Furthermore, we demonstrate the potential of this dataset in gaining insights
into the affective experience and cognitive activity behind user behaviors in
recommender systems. This work presents a novel perspective for enhancing short
video recommendation by leveraging the rich information contained in EEG
signals and multidimensional affective engagement scores, paving the way for
future research in short video recommendation systems.
