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.

翻译：摘要：近年来，短视频平台广泛普及，使视频推荐质量对用户留存至关重要。现有推荐系统主要依赖行为数据，但受数据稀疏性以及偶然交互或个人习惯导致的噪声等问题限制，在推断用户偏好方面面临挑战。为应对这些问题并提供对用户情感体验及认知活动更全面的理解，我们提出了EEG-SVRec——首个在短视频推荐中包含用户多维情感参与标签的脑电图数据集。该研究共纳入30名参与者，收集了3657次交互，为深入探索用户偏好与认知活动提供了丰富的数据集。通过结合自我评估技术与实时低成本的脑电信号，我们更细致地解读了用户的情感体验（效价、唤醒度、沉浸感、兴趣、视觉与听觉维度）及其行为背后的认知机制。我们建立了推荐算法评分预测的基准，显示融合脑电信号后性能显著提升。此外，我们展示了该数据集在揭示推荐系统中用户行为背后的情感体验与认知活动方面的潜力。本研究通过利用脑电信号与多维情感参与评分所蕴含的丰富信息，为增强短视频推荐提供了新视角，为短视频推荐系统的未来研究奠定了基础。