U-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization

• URL: http://arxiv.org/abs/2502.19108v2
• Date: Thu, 10 Jul 2025 03:26:36 GMT
• Title: U-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization
• Authors: Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma, Qinglang Guo, Min Zhang,
• Abstract summary: We introduce User-Sticker, a dataset that includes temporal and user anonymous ID across conversations.<n>The raw data was collected from a popular messaging platform from 67 conversations over 720 hours of crawling.<n>The dataset captures rich temporal, multilingual, and cross-domain behaviors not previously available in other datasets.
• Score: 20.082343227750282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instant messaging with texts and stickers has become a widely adopted communication medium, enabling efficient expression of user semantics and emotions. With the increased use of stickers conveying information and feelings, sticker retrieval and recommendation has emerged as an important area of research. However, a major limitation in existing literature has been the lack of datasets capturing temporal and user-specific sticker interactions, which has hindered further progress in user modeling and sticker personalization. To address this, we introduce User-Sticker, a dataset that includes temporal and user anonymous ID across conversations. It is the largest publicly available sticker dataset to date, containing 22K unique users, 370K stickers, and 8.3M messages. The raw data was collected from a popular messaging platform from 67 conversations over 720 hours of crawling. All text and image data were carefully vetted for safety and privacy checks and modifications. Spanning 10 domains, the U-Sticker dataset captures rich temporal, multilingual, and cross-domain behaviors not previously available in other datasets. Extensive quantitative and qualitative experiments demonstrate U-Sticker's practical applications in user behavior modeling and personalized recommendation and highlight its potential to further research areas in personalized retrieval and conversational studies. U-Sticker dataset is publicly available.

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