EEG-SVRec：一个带有短视频推荐中用户多维情感参与标签的EEG数据集
EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation
AI总结：
本文提出首个带有用户多维情感参与标签的短视频推荐EEG数据集EEG-SVRec，通过融合EEG信号与自我评估，建立评分预测基准并验证其对推荐性能的提升，为理解用户情感与认知机制及改进短视频推荐提供了新视角。
摘要：
近年来，短视频平台获得了广泛普及，使得视频推荐质量对于留住用户至关重要。现有推荐系统主要依赖行为数据，在推断用户偏好时由于数据稀疏以及偶然交互或个人习惯带来的噪声等问题而面临局限。为了应对这些挑战并提供对用户情感体验和认知活动更全面的理解，我们提出了EEG-SVRec，这是第一个在短视频推荐中带有用户多维情感参与标签的EEG数据集。该研究涉及30名参与者，收集了3,657次交互，提供了一个丰富的数据集，可用于更深入地探索用户偏好和认知活动。通过结合自我评估技术和实时、低成本的EEG信号，我们提供了对用户情感体验（效价、唤醒度、沉浸感、兴趣、视觉和听觉）及其行为背后认知机制更详细的理解。我们为推荐算法的评分预测建立了基准，结果显示在引入EEG信号后性能有显著提升。此外，我们展示了该数据集在深入理解推荐系统中用户行为背后的情感体验和认知活动方面的潜力。这项工作通过利用EEG信号和多维情感参与分数中包含的丰富信息，为增强短视频推荐提供了一个新的视角，为未来短视频推荐系统的研究铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
